{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "06_Multilayer_Perceptrons",
      "provenance": [],
      "collapsed_sections": [
        "xqWpi0X2dWpe"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTdCMVl9YAXw",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://practicalai.me\"><img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/rounded_logo.png\" width=\"100\" align=\"left\" hspace=\"20px\" vspace=\"20px\"></a>\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/basic_ml/06_Multilayer_Perceptron/nn.png\" width=\"200\" vspace=\"10px\" align=\"right\">\n",
        "\n",
        "<div align=\"left\">\n",
        "<h1>Multilayer Perceptron (MLP)</h1>\n",
        "\n",
        "In this lesson, we will explore multilayer perceptrons (MLPs) which are a basic type of neural network. We will implement them using Tensorflow with Keras.",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xuabAj4PYj57",
        "colab_type": "text"
      },
      "source": [
        "<table align=\"center\">\n",
        "  <td>\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/rounded_logo.png\" width=\"25\"><a target=\"_blank\" href=\"https://practicalai.me\"> View on practicalAI</a>\n",
        "  </td>\n",
        "  <td>\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/colab_logo.png\" width=\"25\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/06_Multilayer_Perceptron.ipynb\"> Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/github_logo.png\" width=\"22\"><a target=\"_blank\" href=\"https://github.com/practicalAI/practicalAI/blob/master/notebooks/basic_ml/06_Multilayer_Perceptron.ipynb\"> View code on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VoMq0eFRvugb",
        "colab_type": "text"
      },
      "source": [
        "# Overview"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JqxyljU18hvt",
        "colab_type": "text"
      },
      "source": [
        "* **Objective:**  Predict the probability of class $y$ given the inputs $X$. Non-linearity is introduced to model the complex, non-linear data.\n",
        "* **Advantages:**\n",
        "  * Can model non-linear patterns in the data really well.\n",
        "* **Disadvantages:**\n",
        "  * Overfits easily.\n",
        "  * Computationally intensive as network increases in size.\n",
        "  * Not easily interpretable.\n",
        "* **Miscellaneous:** Future neural network architectures that we'll see use the MLP as a modular unit for feed forward operations (affine transformation (XW) followed by a non-linear operation)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWro5T5qTJJL",
        "colab_type": "text"
      },
      "source": [
        "Our goal is to learn a model  𝑦̂   that models  𝑦  given  𝑋 . You'll notice that neural networks are just extensions of the generalized linear methods we've seen so far but with non-linear activation functions since our data will be highly non-linear.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/basic_ml/06_Multilayer_Perceptron/nn.png\" width=\"550\">\n",
        "\n",
        "$z_1 = XW_1$\n",
        "\n",
        "$a_1 = f(z_1)$\n",
        "\n",
        "$z_2 = a_1W_2$\n",
        "\n",
        "$\\hat{y} = softmax(z_2)$ # classification\n",
        "\n",
        "* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n",
        "* $W_1$ = 1st layer weights | $\\in \\mathbb{R}^{DXH}$ ($H$ is the number of hidden units in layer 1)\n",
        "* $z_1$ = outputs from first layer  $\\in \\mathbb{R}^{NXH}$\n",
        "* $f$ = non-linear activation function\n",
        "*nn $a_1$ = activation applied first layer's outputs | $\\in \\mathbb{R}^{NXH}$\n",
        "* $W_2$ = 2nd layer weights | $\\in \\mathbb{R}^{HXC}$ ($C$ is the number of classes)\n",
        "* $z_2$ = outputs from second layer  $\\in \\mathbb{R}^{NXH}$\n",
        "* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NXC}$ ($N$ is the number of samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GHB1Qi3sskB",
        "colab_type": "text"
      },
      "source": [
        "**Note**: We're going to leave out the bias terms $\\beta$ to avoid further crowding the backpropagation calculations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jq65LZJbSpzd",
        "colab_type": "text"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfi_YArvjzrg",
        "colab_type": "text"
      },
      "source": [
        "1. Randomly initialize the model's weights $W$ (we'll cover more effective initialization strategies later in this lesson).\n",
        "2. Feed inputs $X$ into the model to do the forward pass and receive the probabilities.\n",
        "  * $z_1 = XW_1$\n",
        "  * $a_1 = f(z_1)$\n",
        "  * $z_2 = a_1W_2$\n",
        "  * $\\hat{y} = softmax(z_2)$\n",
        "3. Compare the predictions $\\hat{y}$ (ex.  [0.3, 0.3, 0.4]]) with the actual target values $y$ (ex. class 2 would look like [0, 0, 1]) with the objective (cost) function to determine loss $J$. A common objective function for classification tasks is cross-entropy loss. \n",
        "  * $J(\\theta) = - \\sum_i y_i ln (\\hat{y_i}) $\n",
        "  * Since each input maps to exactly one class, our cross-entropy loss simplifies to: \n",
        "  * $J(\\theta) = - \\sum_i ln(\\hat{y_i}) = - \\sum_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) $\n",
        "4. Calculate the gradient of loss $J(\\theta)$ w.r.t to the model weights. \n",
        " * $\\frac{\\partial{J}}{\\partial{W_{2j}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}0 - e^{a_1W_{2y}}e^{a_1W_{2j}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{a_1e^{a_1W_{2j}}}{\\sum_j e^{a_1W}} = a_1\\hat{y}$\n",
        "  * $\\frac{\\partial{J}}{\\partial{W_{2y}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}a_1 - e^{a_1W_{2y}}e^{a_1W_{2y}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{1}{\\hat{y}}(a_1\\hat{y} - a_1\\hat{y}^2) = a_1(\\hat{y}-1)$\n",
        "  * $ \\frac{\\partial{J}}{\\partial{W_1}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}} \\frac{\\partial{\\hat{y}}}{\\partial{a_2}}  \\frac{\\partial{a_2}}{\\partial{z_2}}  \\frac{\\partial{z_2}}{\\partial{W_1}}  = W_2(\\partial{scores})(\\partial{ReLU})X $\n",
        "   \n",
        "5. Update the weights $W$ using a small learning rate $\\alpha$. The updates will penalize the probabiltiy for the incorrect classes (j) and encourage a higher probability for the correct class (y).\n",
        "  * $W_i = W_i - \\alpha\\frac{\\partial{J}}{\\partial{W_i}}$\n",
        "6. Repeat steps 2 - 4 until model performs well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmvSOsB-CuJb",
        "colab_type": "text"
      },
      "source": [
        "# Set up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpFe3zVoCuTD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2ea0d1dd-9e5e-4c13-bb66-2859f2858ef8"
      },
      "source": [
        "# Use TensorFlow 2.x\n",
        "%tensorflow_version 2.x"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PXdZbDzCuRE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-NnTT8LDjf3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Arguments\n",
        "SEED = 1234\n",
        "SHUFFLE = True\n",
        "DATA_FILE = \"spiral.csv\"\n",
        "INPUT_DIM = 2\n",
        "NUM_CLASSES = 3\n",
        "NUM_SAMPLES_PER_CLASS = 500\n",
        "TRAIN_SIZE = 0.7\n",
        "VAL_SIZE = 0.15\n",
        "TEST_SIZE = 0.15\n",
        "NUM_EPOCHS = 10\n",
        "BATCH_SIZE = 32\n",
        "HIDDEN_DIM = 100\n",
        "LEARNING_RATE = 1e-2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nB7mfVd7Dn9e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set seed for reproducibility\n",
        "np.random.seed(SEED)\n",
        "tf.random.set_seed(SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XtKqNioAayCy",
        "colab_type": "text"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3OrtMpFayFC",
        "colab_type": "text"
      },
      "source": [
        "Download non-linear spiral data for a classification task."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NfIz_4OPYpG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import urllib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q14QCqKxUS2v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Upload data from GitHub to notebook's local drive\n",
        "url = \"https://raw.githubusercontent.com/practicalAI/practicalAI/master/data/spiral.csv\"\n",
        "response = urllib.request.urlopen(url)\n",
        "html = response.read()\n",
        "with open(DATA_FILE, 'wb') as fp:\n",
        "    fp.write(html)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efS3lVYETA17",
        "colab_type": "code",
        "outputId": "d11485e5-fc58-4cc2-8220-1f955b7ab41f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Load data\n",
        "df = pd.read_csv(DATA_FILE, header=0)\n",
        "X = df[['X1', 'X2']].values\n",
        "y = df['color'].values\n",
        "df.head(5)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>color</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>c1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.000457</td>\n",
              "      <td>0.001951</td>\n",
              "      <td>c1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001194</td>\n",
              "      <td>0.003826</td>\n",
              "      <td>c1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.000231</td>\n",
              "      <td>0.006008</td>\n",
              "      <td>c1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.000896</td>\n",
              "      <td>0.007966</td>\n",
              "      <td>c1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         X1        X2 color\n",
              "0  0.000000  0.000000    c1\n",
              "1 -0.000457  0.001951    c1\n",
              "2  0.001194  0.003826    c1\n",
              "3 -0.000231  0.006008    c1\n",
              "4 -0.000896  0.007966    c1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmTwuA9kHjhA",
        "colab_type": "code",
        "outputId": "4f046fb6-2eb2-4a15-aaee-186b9619ba3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print (\"X: \", np.shape(X))\n",
        "print (\"y: \", np.shape(y))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X:  (1500, 2)\n",
            "y:  (1500,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgVjStv8VnX2",
        "colab_type": "code",
        "outputId": "51bfa28a-35fe-4fbf-f99e-2b1eb8ea8cbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# Visualize data\n",
        "plt.title(\"Generated non-linear data\")\n",
        "colors = {'c1': 'red', 'c2': 'yellow', 'c3': 'blue'}\n",
        "plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], edgecolors='k', s=25)\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5c9pZpYqLAQF2fvfddyTJqVOnslkzm9czW7WycuLEiRnVDE8NI0eOpBADvISU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\n3bqVM2bM4NKlS//W5ejXQt988w1drigaDI9QUEJ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U/pZqjI/AG2rbZdpsXh45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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f01ldQXXS3lc",
        "colab_type": "text"
      },
      "source": [
        "# Split data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38DagTg9S4ze",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import collections\n",
        "import json\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OkayxS6pDbTm",
        "colab_type": "text"
      },
      "source": [
        "### Components"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRVOgVxDDcx3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_val_test_split(X, y, val_size, test_size, shuffle):\n",
        "    \"\"\"Split data into train/val/test datasets.\n",
        "    \"\"\"\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size=test_size, stratify=y, shuffle=shuffle)\n",
        "    X_train, X_val, y_train, y_val = train_test_split(\n",
        "        X_train, y_train, test_size=val_size, stratify=y_train, shuffle=shuffle)\n",
        "    return X_train, X_val, X_test, y_train, y_val, y_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ouKZ4w95DdR3",
        "colab_type": "text"
      },
      "source": [
        "### Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGFqcqTDXhkl",
        "colab_type": "code",
        "outputId": "89bafff9-a755-449b-d1e1-4ffda1026c6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Create data splits\n",
        "X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n",
        "    X=X, y=y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n",
        "class_counts = dict(collections.Counter(y))\n",
        "print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n",
        "print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n",
        "print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n",
        "print (f\"X_train[0]: {X_train[0]}\")\n",
        "print (f\"y_train[0]: {y_train[0]}\")\n",
        "print (f\"Classes: {class_counts}\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train: (1083, 2), y_train: (1083,)\n",
            "X_val: (192, 2), y_val: (192,)\n",
            "X_test: (225, 2), y_test: (225,)\n",
            "X_train[0]: [ 0.23623443 -0.59618506]\n",
            "y_train[0]: c1\n",
            "Classes: {'c1': 500, 'c3': 500, 'c2': 500}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8EQEwO2OWfN",
        "colab_type": "text"
      },
      "source": [
        "# Label encoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ldFOhAqObcd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import json\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYjAYlTdObf0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Output vectorizer\n",
        "y_tokenizer = LabelEncoder()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VO1uxrdgObkf",
        "colab_type": "code",
        "outputId": "d86f84ee-fd62-4f90-92f9-fc21ae4b1c97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Fit on train data\n",
        "y_tokenizer = y_tokenizer.fit(y_train)\n",
        "classes = list(y_tokenizer.classes_)\n",
        "print (f\"classes: {classes}\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classes: ['c1', 'c2', 'c3']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1t9vjih9Oeq9",
        "colab_type": "code",
        "outputId": "4c789e43-f940-46d7-f319-be999f6b1bd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Convert labels to tokens\n",
        "print (f\"y_train[0]: {y_train[0]}\")\n",
        "y_train = y_tokenizer.transform(y_train)\n",
        "y_val = y_tokenizer.transform(y_val)\n",
        "y_test = y_tokenizer.transform(y_test)\n",
        "print (f\"y_train[0]: {y_train[0]}\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "y_train[0]: c1\n",
            "y_train[0]: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVgNHgSkInPE",
        "colab_type": "code",
        "outputId": "fdffc411-2b4f-4f7f-d6d7-d3ef41c5ee0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Class weights\n",
        "counts = collections.Counter(y_train)\n",
        "class_weights = {_class: 1.0/count for _class, count in counts.items()}\n",
        "print (f\"class counts: {counts},\\nclass weights: {class_weights}\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "class counts: Counter({0: 361, 2: 361, 1: 361}),\n",
            "class weights: {0: 0.002770083102493075, 2: 0.002770083102493075, 1: 0.002770083102493075}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDFM5gQte5rQ",
        "colab_type": "text"
      },
      "source": [
        "# Standardize data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JlmiwCUre_ZH",
        "colab_type": "text"
      },
      "source": [
        "We need to standardize our data (zero mean and unit variance) in order to optimize quickly. We're only going to standardize the inputs X because out outputs y are class values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4uCG3vMe52J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tTbwUOme5z7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Standardize the data (mean=0, std=1) using training data\n",
        "X_scaler = StandardScaler().fit(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGTQNaRie5xb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Apply scaler on training and test data (don't standardize outputs for classification)\n",
        "standardized_X_train = X_scaler.transform(X_train)\n",
        "standardized_X_val = X_scaler.transform(X_val)\n",
        "standardized_X_test = X_scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dstH0Cm-fLgK",
        "colab_type": "code",
        "outputId": "2b8ad00c-e107-448c-c651-8f9c841bce06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Check\n",
        "print (f\"standardized_X_train: mean: {np.mean(standardized_X_train, axis=0)[0]}, std: {np.std(standardized_X_train, axis=0)[0]}\")\n",
        "print (f\"standardized_X_val: mean: {np.mean(standardized_X_val, axis=0)[0]}, std: {np.std(standardized_X_val, axis=0)[0]}\")\n",
        "print (f\"standardized_X_test: mean: {np.mean(standardized_X_test, axis=0)[0]}, std: {np.std(standardized_X_test, axis=0)[0]}\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "standardized_X_train: mean: 2.0051673464144102e-16, std: 1.0000000000000002\n",
            "standardized_X_val: mean: 0.10067009920283043, std: 1.020838918120433\n",
            "standardized_X_test: mean: 0.1287588265908901, std: 0.9647767079653526\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHofozO7RIiV",
        "colab_type": "text"
      },
      "source": [
        "# Linear model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlVmr5XkRMCf",
        "colab_type": "text"
      },
      "source": [
        "Before we get to our neural network, we're going to implement a generalized linear model (logistic regression) first to see why linear models won't suffice for our dataset. We will use Tensorflow with Keras to do this. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0QY6jd-xHvi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m4Y8vYNxV_L5",
        "colab_type": "text"
      },
      "source": [
        "### Components"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTUJhxkcyOJb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Linear model\n",
        "class LogisticClassifier(Model):\n",
        "    def __init__(self, hidden_dim, num_classes):\n",
        "        super(LogisticClassifier, self).__init__()\n",
        "        self.fc1 = Dense(units=hidden_dim, activation='linear') # linear = no activation function\n",
        "        self.fc2 = Dense(units=num_classes, activation='softmax')\n",
        "        \n",
        "    def call(self, x_in, training=False):\n",
        "        \"\"\"Forward pass.\"\"\"\n",
        "        z = self.fc1(x_in)\n",
        "        y_pred = self.fc2(z)\n",
        "        return y_pred\n",
        "    \n",
        "    def sample(self, input_shape):\n",
        "        x_in = Input(shape=input_shape)\n",
        "        return Model(inputs=x_in, outputs=self.call(x_in)).summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JcfmjyWHBKeA",
        "colab": {}
      },
      "source": [
        "def plot_confusion_matrix(y_true, y_pred, classes, cmap=plt.cm.Blues):\n",
        "    \"\"\"Plot a confusion matrix using ground truth and predictions.\"\"\"\n",
        "    # Confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    #  Figure\n",
        "    fig = plt.figure()\n",
        "    ax = fig.add_subplot(111)\n",
        "    cax = ax.matshow(cm, cmap=plt.cm.Blues)\n",
        "    fig.colorbar(cax)\n",
        "\n",
        "    # Axis\n",
        "    plt.title(\"Confusion matrix\")\n",
        "    plt.ylabel(\"True label\")\n",
        "    plt.xlabel(\"Predicted label\")\n",
        "    ax.set_xticklabels([''] + classes)\n",
        "    ax.set_yticklabels([''] + classes)\n",
        "    ax.xaxis.set_label_position('bottom') \n",
        "    ax.xaxis.tick_bottom()\n",
        "\n",
        "    # Values\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, f\"{cm[i, j]:d} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    # Display\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdzQ8LVuGJL3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_multiclass_decision_boundary(model, X, y, savefig_fp=None):\n",
        "    \"\"\"Plot the multiclass decision boundary for a model that accepts 2D inputs.\n",
        "\n",
        "    Arguments:\n",
        "        model {function} -- trained model with function model.predict(x_in).\n",
        "        X {numpy.ndarray} -- 2D inputs with shape (N, 2).\n",
        "        y {numpy.ndarray} -- 1D outputs with shape (N,).\n",
        "    \"\"\"\n",
        "    # Axis boundaries\n",
        "    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101),\n",
        "                         np.linspace(y_min, y_max, 101))\n",
        "\n",
        "    # Create predictions\n",
        "    x_in = np.c_[xx.ravel(), yy.ravel()]\n",
        "    y_pred = model.predict(x_in)\n",
        "    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n",
        "\n",
        "    # Plot decision boundary\n",
        "    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "    plt.xlim(xx.min(), xx.max())\n",
        "    plt.ylim(yy.min(), yy.max())\n",
        "\n",
        "    # Plot\n",
        "    if savefig_fp:\n",
        "        plt.savefig(savefig_fp, format='png')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqNeggOsWEE8",
        "colab_type": "text"
      },
      "source": [
        "### Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApoN49qmRhl5",
        "colab_type": "code",
        "outputId": "b22092f8-c909-4edc-e1d6-814edd868ecb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Initialize the model\n",
        "model = LogisticClassifier(hidden_dim=HIDDEN_DIM,\n",
        "                           num_classes=NUM_CLASSES)\n",
        "model.sample(input_shape=(INPUT_DIM,))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 2)]               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 100)               300       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 303       \n",
            "=================================================================\n",
            "Total params: 603\n",
            "Trainable params: 603\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJXjloy30PVU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Compile\n",
        "model.compile(optimizer=Adam(lr=LEARNING_RATE),\n",
        "              loss=SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weDSM-VRC8NX",
        "colab_type": "code",
        "outputId": "d8b0848b-83bd-4bc9-9246-7127650b370b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Training\n",
        "model.fit(x=standardized_X_train, \n",
        "          y=y_train,\n",
        "          validation_data=(standardized_X_val, y_val),\n",
        "          epochs=NUM_EPOCHS,\n",
        "          batch_size=BATCH_SIZE,\n",
        "          class_weight=class_weights,\n",
        "          shuffle=False,\n",
        "          verbose=1)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1083 samples, validate on 192 samples\n",
            "Epoch 1/10\n",
            "1083/1083 [==============================] - 2s 2ms/sample - loss: 0.0022 - accuracy: 0.5199 - val_loss: 0.0021 - val_accuracy: 0.4948\n",
            "Epoch 2/10\n",
            "1083/1083 [==============================] - 0s 143us/sample - loss: 0.0021 - accuracy: 0.5254 - val_loss: 0.0021 - val_accuracy: 0.5052\n",
            "Epoch 3/10\n",
            "1083/1083 [==============================] - 0s 138us/sample - loss: 0.0021 - accuracy: 0.5263 - val_loss: 0.0021 - val_accuracy: 0.5104\n",
            "Epoch 4/10\n",
            "1083/1083 [==============================] - 0s 143us/sample - loss: 0.0021 - accuracy: 0.5254 - val_loss: 0.0021 - val_accuracy: 0.5104\n",
            "Epoch 5/10\n",
            "1083/1083 [==============================] - 0s 138us/sample - loss: 0.0021 - accuracy: 0.5254 - val_loss: 0.0021 - val_accuracy: 0.5000\n",
            "Epoch 6/10\n",
            "1083/1083 [==============================] - 0s 163us/sample - loss: 0.0021 - accuracy: 0.5254 - val_loss: 0.0021 - val_accuracy: 0.5000\n",
            "Epoch 7/10\n",
            "1083/1083 [==============================] - 0s 140us/sample - loss: 0.0021 - accuracy: 0.5235 - val_loss: 0.0021 - val_accuracy: 0.5000\n",
            "Epoch 8/10\n",
            "1083/1083 [==============================] - 0s 138us/sample - loss: 0.0021 - accuracy: 0.5226 - val_loss: 0.0021 - val_accuracy: 0.5000\n",
            "Epoch 9/10\n",
            "1083/1083 [==============================] - 0s 143us/sample - loss: 0.0021 - accuracy: 0.5235 - val_loss: 0.0021 - val_accuracy: 0.5052\n",
            "Epoch 10/10\n",
            "1083/1083 [==============================] - 0s 143us/sample - loss: 0.0021 - accuracy: 0.5245 - val_loss: 0.0021 - val_accuracy: 0.5052\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1dc0374ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Au0tESHKC8Rj",
        "colab_type": "code",
        "outputId": "0361814b-2448-4c18-ca42-3e7ed0008398",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Predictions\n",
        "pred_train = model.predict(standardized_X_train) \n",
        "pred_test = model.predict(standardized_X_test)\n",
        "print (f\"sample probability: {pred_test[0]}\")\n",
        "pred_train = np.argmax(pred_train, axis=1)\n",
        "pred_test = np.argmax(pred_test, axis=1)\n",
        "print (f\"sample class: {pred_test[0]}\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample probability: [0.02820205 0.13940957 0.8323884 ]\n",
            "sample class: 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pKE1ZfJRb1b4",
        "colab_type": "code",
        "outputId": "e50b3012-1cdd-456c-9633-7719d2a1c22a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Accuracy\n",
        "train_acc = accuracy_score(y_train, pred_train)\n",
        "test_acc = accuracy_score(y_test, pred_test)\n",
        "print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 0.53, test acc: 0.54\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3NVCzLWsCe3",
        "colab_type": "code",
        "outputId": "25a61170-24b4-4f46-ef35-979a3840b76e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "# Metrics\n",
        "plot_confusion_matrix(y_test, pred_test, classes=classes)\n",
        "print (classification_report(y_test, pred_test))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cOZJ/JyAHxMklO8aY1cYYMcY0NsaE2pbFxpgBxphGtvXd07TmMuVMgAswxiy1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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.51      0.59      0.55        75\n",
            "           1       0.50      0.44      0.47        75\n",
            "           2       0.60      0.59      0.59        75\n",
            "\n",
            "    accuracy                           0.54       225\n",
            "   macro avg       0.54      0.54      0.54       225\n",
            "weighted avg       0.54      0.54      0.54       225\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGfevv1kusw8",
        "colab_type": "code",
        "outputId": "93eb89f4-ee69-4cb3-d289-d7b46d772580",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ecx30X+hk/PWZqvPZlmTqxgI9Z9q1FZsDR0MEb6Zbhci7YzulTThhiHIBXDiH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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3OK8p-Ng3BC",
        "colab_type": "text"
      },
      "source": [
        "# Activation functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghf5uLuhg3D0",
        "colab_type": "text"
      },
      "source": [
        "Using the generalized linear method (logistic regression) yielded poor results because of the non-linearity present in our data. We need to use an activation function that can allow our model to learn and map the non-linearity in our data. There are many different options so let's explore a few."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZCBe8vOytx5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.activations import relu\n",
        "from tensorflow.keras.activations import sigmoid\n",
        "from tensorflow.keras.activations import tanh"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivnfSKEhg3Md",
        "colab_type": "code",
        "outputId": "0cce6254-2647-42c6-e2a1-7197cc29bb55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "# Fig size\n",
        "plt.figure(figsize=(12,3))\n",
        "\n",
        "# Data\n",
        "x = np.arange(-5., 5., 0.1)\n",
        "\n",
        "# Sigmoid activation (constrain a value between 0 and 1.)\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.title(\"Sigmoid activation\")\n",
        "y = sigmoid(x)\n",
        "plt.plot(x, y)\n",
        "\n",
        "# Tanh activation (constrain a value between -1 and 1.)\n",
        "plt.subplot(1, 3, 2)\n",
        "y = tanh(x)\n",
        "plt.title(\"Tanh activation\")\n",
        "plt.plot(x, y)\n",
        "\n",
        "# Relu (clip the negative values to 0)\n",
        "plt.subplot(1, 3, 3)\n",
        "y = relu(x)\n",
        "plt.title(\"ReLU activation\")\n",
        "plt.plot(x, y)\n",
        "\n",
        "# Show plots\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fP8nrcAxNmvf7V8Cv2jou0zYWb8lhYI/ODE22m2gYY7zz1Mc72JlfwQvfnxxy\nA+SChW5kJuSpKvfM30R8TCQ3n32c1+EYE/Yqa/x8mpHHGSN72W3NjTGeySqs4JHF6cwa24fpI0J7\nsLUVwqbZlmzN4eP0PG44cwTd42O8DseYsPfZ9jyqfQFm2PSFxhgP/W6+M/TkN+eH/ixSVgibZqn1\nB/jdW5sZkhzP5VMHeh2OMQZ4f0sO8TGRTB7c3etQjDFh6qNtubyzcT/Xnz6MlBAdIBfMCmHTLHOX\n7yYzt5xfzxpFdKT9GRnjNVXlw625nDK8Z0j3xzPGdFx1A+QG9ejMtdNDd4BcMPu0NEettKqWhxal\nc9KQ7swY1cvrcIwxQGZeOdlFlSHfH88Y03E9/ckOMvPKuWP2GGKjIr0Op0ls1ghz1J74cDv55TU8\nd+5oG5BjTIj4aJtzG+zpw60QNsa0vb1FlTzyfgZnje7N6ce1n0YyaxE2RyWnpIqnP9nB7OP7Ma5/\notfhGGNcH6fnMbhnPKndO3sdijEmDN371mYCqtzeDgbIBbNC2ByVh99Px+dXbjp7hNehGGNc1T4/\nn2/P59ThPb0OxRgThj7NyOOt9fv40WnD2t3JuBXCpsl25JUzd8UevjNlAAN72GT9xoSKVbsKqaz1\nW7cIY0ybq/EFuGPeRgZ078z/fa19DJALZoWwabIHF24jJjKC688Y5nUoxpggH6fnERUhnDS0h9eh\nGGPCzHOf7SAjp4w7LhhNXHT7GCAXzAph0yRb9pfw5rq9XDVtEL0S4rwOxxgT5LOMPCYOSKJLrI1/\nNsa0nQMlVTy8KJ0zR/Vixqj2eSMfK4RNk/z5vW10iYniunYyL6Ax4aK4spb12cWcPNT6B3dUIhIn\nIstFZK2IbBSRu7yOyRhwBsjVBpTbzx/jdSjNZoWwOaK1e4pYuOkA104fQlJnu5WyMaFkWWY+AYWT\nrVtER1YNnKGqxwMTgJkicpLHMZkw99n2POat3csPvzaUAT3a1wC5YE0qhEVkpohsFZEMEbm1kW2+\nJSKb3LPVf7VsmMZLDy7aRlLnaK6aNsjrUIwx9Xy2PZ+46AgmDujmdSimlaijzH0a7f6ohyGZMFfr\nD3DHGxtJ7d6JH5421OtwjskRO5SJSCTwGHAWkAWsEJF5qropaJvhwK+AaapaKCLtZyZlc1irdxfy\nwdZcbpl5HAlx0V6HY4yp59OMPE4c1N1uq9zBud/Fq4BhwGOquszjkEwYe/6znaTnlPGPy9Pa5QC5\nYE355JwMZKhqpqrWAHOBOfW2uRYnMQsBVDWnZcM0Xnlw4Ta6x8dwxdRBXodijKknp7SK9Jwypg2z\n/sEdnar6VXUC0B+YLCJj6671w5EAACAASURBVG8jIteJyEoRWZmbm9v2QZqwkFNSxUOL0jntuGTO\nHNX+2z2bUginAHuCnme5y4KNAEaIyKcislREZja0I0vS9mXVrkI+Ts/juulDiLfR6MaEnM+35wPW\nPzicqGoRsAQ45HtWVZ9U1TRVTUtOtjmlTev4/dtbqPEFuPOCMYiI1+Ecs5a6lhYFDAdOAy4F/iEi\nSfU3siRtXx5+P53u8TFcPnWg16EYYxqwNLOAhNgoxvSz2513ZCKSXPedKiKdcLoqbvE2KhOOlu8o\n4PU12Vw3fQiDenaMG2s1pRDOBlKDnvd3lwXLAuapaq2q7gC24RTGpp1as7uQj7blct30IXSOsdZg\nY0LRssx8Jg/uTmRE+2+VMYfVF1giIuuAFcBCVZ3vcUwmzPj8AW5/YwMpSZ340ente4BcsKZUOCuA\n4SIyGKcAvgT4Tr1t/ofTEvysiPTE6SqR2ZKBmrb1V7c1+HsnWWuwMaHoQEkVmXnlXDp5gNehmFam\nquuAiV7HYcLbC5/vYsv+Up747qQO1UB2xBZhVfUB1wPvApuBV1R1o4jcLSKz3c3eBfJFZBNO36Vf\nqGp+awVtWte6rCKWbM3l6lMGW99gY0LU0kznI/akIdY/2BjTunJLq3lw4Tamj0jmnDF9vA6nRTWp\nylHVBcCCestuD3qswM/dH9POPbI4g8RO0dY32JgQVtc/eHS/rl6HYozp4H7/9maqfH7uvGB0hxgg\nF8wmnjQH2bS3hIWbDnDVtEE2b7AxIWzZjnxOtP7BxphWtnJnAf9dnc21pw5hSHIXr8NpcVYIm4M8\nuiSdLrFRXHXyYK9DMcY0Iqekiszcck4a0t3rUIwxHZjPH+C3b2ykb2Ic158xzOtwWoUVwuZLGTml\nvL1hP5dPHUhiZ2sNNiZULdtRAMCUwdY/2BjTel5atpvN+0r4zXmjO9QAuWBWCJsv/W3JduKiIrn6\nFGsNNiaULduRT5fYKMZY/2BjTCvJK6vmT+9tZdqwHpw7rmMNkAtmhbABYFd+OW+s3ctlUwbQo0us\n1+EYYw5jWWYBJwzsRlSkfYQbY1rHA29vobLGz12zO8Yd5Bpjn6IGgCc+3E5khHDt9CFeh2KMOYz8\nsmrSc8qYPNj6BxtjWseqXYW8uiqLq08ZzLBeCV6H06qsEDbsK67ktVVZfCutP727xnkdjjlGIjJT\nRLaKSIaI3NrA+lgR+be7fpmIDGr7KE1zrdjp9A+2gXLGmNbgDyh3zNtAn65x/GRGx79JsBXChr9/\nmIkq/N/0jnPLxHAlIpHAY8AsYDRwqYiMrrfZ1UChqg4DHgQeaNsozbFYmllAXHQE41KSvA7FGNMB\n/Wv5bjZkl3DbeaPoEgY31bJCOMzllVUzd8VuLpyYQmr3zl6HY47dZCBDVTNVtQaYC8ypt80c4Hn3\n8WvADOnIHcA6mOU7Cpg0oBsxUfbxbYxpWQXlNfzp3a1MHdKD88f39TqcNmGfpGHu6U92UO0L8MPT\nrDW4g0gB9gQ9z3KXNbiNewv1YuCQebhE5DoRWSkiK3Nzc1spXHM0iitq2by/xKZNM8a0ij++u4Xy\nah93z+nYA+SCWSEcxooravnn57s4d1xfhnbAu8WYY6OqT6pqmqqmJScnex2OwekfrApTrH+wMaaF\nfbGniLkr9nDVtEEM792xB8gFs0I4jD332U7Kqn1cf3rHvFtMmMoGUoOe93eXNbiNiEQBiUB+m0Rn\njsnynQXEREYwIdX6BxtjWo4/oNz+xgaSu8Ty0zAYIBfMCuEwVV7t49nPdnDmqF6M6muT8ncgK4Dh\nIjJYRGKAS4B59baZB1zhPv4GsFhVtQ1jNM20LDOfCalJxEVHeh2KMaYD+feKPazLKua280aREBde\nd5a1QjhMvbRsF0UVtfzYWoM7FLfP7/XAu8Bm4BVV3Sgid4vIbHezp4EeIpIB/Bw4ZIo1E3rKqn1s\n2Fti3SKMMS2qsLyGP767hSmDuzP7+H5eh9PmOv68GOYQVbV+nvxoB6cM68nEAd28Dse0MFVdACyo\nt+z2oMdVwDfbOi5zbFbtKsQfULuRhjGmRf3xva2UVPm4K4wGyAWzFuEw9O8Ve8grq+b6M6w12Jj2\nYllmPlERwgkD7eTVGNMy1mcV8/Ly3VwxdRAj+4RnN0krhMNMjS/A3z/czomDujHFWpaMaTeW7Shg\nbEoinWPsQp4x5tgFAspv39hAj/hYbjwrvAbIBbNCOMz8Z3UWe4uruP6M4WF5CcSY9qiixse6rCJO\nGmLzBxtjWsarq/bwxZ4ifn3uSLqG2QC5YFYIh5Faf4C/fZDB8f0TmT68p9fhGGOaaPWuImr9ykk2\nUM4Y0wKKKmp44J2tnDioGxdNrH/PpfBihXAYeeOLvewpqOQn1hpsTLuyNDOfyAghbZAVwuFIRFJF\nZImIbBKRjSJyg9cxmfbtz+9to6iihrtmjw37esA6m4UJnz/AY0syGN23KzNG9fI6HGPMUViamc+4\nlES6xNpHdpjyATep6moRSQBWichCVd3kdWCm/dmQXcxLy3Zx+dRBjO4XngPkgjWpRVhEZorIVhHJ\nEJFG5xwVkYtFREUkreVCNC3hzXV72ZFXzk9nDAv7sz9j2pOKGh9rrX9wWFPVfaq62n1cijNHeHhf\nzzbNEnDvINc9PoafnTXC63BCwhELYRGJBB4DZgGjgUtFZHQD2yUANwDLWjpIc2z8AeWRxRmM7JPA\n2aP7eB2OMeYoWP9gE0xEBgETse9a0wz/WZ3F6t1F/HLmSBI7he8AuWBNaRGeDGSoaqaq1gBzgTkN\nbHcP8ABQ1YLxmRYwf91eMnPLuWHGcCIirDXYmPbk88w86x9sABCRLsB/gBtVtaSB9deJyEoRWZmb\nm9v2AZqQVlxZy/1vb2HSgCQuntTf63BCRlMK4RRgT9DzLOpdkhGRSUCqqr51uB1ZkrY9nz/Aw4vS\nOa53AueMsdZgY9qbz7db/2ADIhKNUwS/pKr/bWgbVX1SVdNUNS05ObltAzQh78GF2yisqOHuOWOt\nUSzIMc8aISIRwF+Am460rSVp25u3di+ZeeX87CxrDTamvSmtqmVtVjHThln/4HAmzsCOp4HNqvoX\nr+Mx7c+mvSW88PlOLpsykLEpiV6HE1KaUghnA6lBz/u7y+okAGOBD0RkJ3ASMM8GzHnP5w/w1/fT\nGd23q/UNNqYdWrGzAH9AOXmozfsd5qYB3wPOEJEv3J9zvQ7KtA+qyh3zNpDUOYabzz7O63BCTlOu\nta0AhovIYJwC+BLgO3UrVbUY+PJTWkQ+AG5W1ZUtG6o5Wv9dk83O/Aqe/N4J1hpsTDv0aUY+MVER\nnDCwm9ehGA+p6ieAfYibZnl9TTYrdhbywMXjSOxsA+TqO2KLsKr6gOuBd3GmbHlFVTeKyN0iMru1\nAzTNU+3z8/CidMb3T+Ss0b29DscY0wyfbc/nhAHdiIuO9DoUY0w7VFJVy30LtjAhNYlvnpB65BeE\noSaNvlDVBcCCestub2Tb0449LHOsXlmxh+yiSu77+jibN9iYdii/rJrN+0q4+Wyb69MY0zwPLUwn\nv7yaZ65MsyvDjbBbLHdAlTV+HlmcweRB3Zk+3PoWGtMeLc0sAODkYZbDxpijt2V/Cc9/vpNLJw9g\nfP8kr8MJWVYId0DPf76TnNJqbjp7hLUGG9NOfZyeS0JsFONthLcx5iipKne8sZGEuCh+YQPkDssK\n4Q6muKKWvy3J4PTjkplit2Q1pl1SVT5Oz+PkYT2IirSPaWPM0Zm3di/LdhRwyzkj6RYf43U4Ic0+\nYTuYJz7aTmm1j1tmjvQ6FGNMM23PLSe7qJLpI2y+dWPM0SmtquXetzYzvn8i3z7RBsgdid2qqAPZ\nV1zJs5/uYM7x/RjVt6vX4RhjmunjdOfOm9OHWyFsjDk6f30/ndyyap68PI1IGyB3RNYi3IH8+b1t\nBAJwk/UHMqZd+2hbLoN7xpPavbPXoRhj2pFtB0p59tOdfDstlQmpNkCuKawQ7iA27S3hP6uzuHLa\nIPvyNKYdq/b5WZpZwKk244sx5iioKre/sYH42CjrHnkUrBDuAFSV+xZsJrFTND8+bZjX4RhjjsHK\nnYVU1vqtW4Qx5qi8uW4fSzMLuPmc4+huA+SazArhDuD9zTl8kpHHDTOG2+0TjWnn3t+cQ0xUBCcP\ns1lfjDFNU1bt4963NjE2pSvfmTzA63DaFRss187V+ALcu2AzQ5Pj+e5JA70OxxhzDFSV97cc4OSh\nPegcYx/PxpimeWRxOgdKqvnbZSfYALmjZC3C7dzzn+1kR145vzl/NNE236gx7VpmXjm78iuYMbKX\n16EYY9qJjJwynvlkB988oT8nDOzmdTjtjlVO7VhOSRUPv5/O6cclc/px9sVpTHu3eHMOAKdbIWyM\naQJV5c55G4mLjuSXs2yAXHNYIdyO3bdgMzX+AHfOHuN1KMaYFvD+lgOM7JNA/24284sx5sje3rCf\nTzLyuPns4+jZJdbrcNolK4TbqaWZ+fzvi738YPoQBvaI9zocEwJEpLuILBSRdPffBq+RiYhfRL5w\nf+a1dZymYUUVNazYWcgZ1hpsjGmCihofv5u/iVF9u3LZFBsg11xWCLdD1T4/t72+ntTunfihTZdm\nvnIr8L6qDgfed583pFJVJ7g/s9suPHM4CzcdwB9QzhnTx+tQjDHtwKOLM9hbXMU9c8YQZWOEms3e\nuXboiQ8y2Z5bzj1zxtIpJtLrcEzomAM87z5+HrjQw1jMUXpnw35Skjoxvn+i16EYY0JcZm4Z//g4\nk69PSiFtUHevw2nXrBBuZzJyynhsSQYXHN+P02yAnDlYb1Xd5z7eD/RuZLs4EVkpIktFpNFiWUSu\nc7dbmZub2+LBmq+UVtXycXoeM8f2QcSmPjLGNE5VufPNTcRFRfKrWaO8Dqfds4kq2xF/QLnltbV0\nionkt+fbH384EpFFQEPXzm8LfqKqKiLayG4Gqmq2iAwBFovIelXdXn8jVX0SeBIgLS2tsX2ZFrB4\nSw41/gDnjrNuEcaYw3t34wE+2pbL7eePJjnBBsgdKyuE25HnPtvJ6t1F/OVbx9MrIc7rcIwHVPXM\nxtaJyAER6auq+0SkL5DTyD6y3X8zReQDYCJwSCFs2s7b6/fTu2ssE1NtDlBjTOMqa/zcM38TI/sk\ncPlUu4lWS7CuEe1EZm4Zf3x3C2eM7MVFE1O8DseEpnnAFe7jK4A36m8gIt1EJNZ93BOYBmxqswjN\nIYora1m8NYdZY/sSYXeEMsYcxmNLMsguquTuOWNtgFwLsXexHaj1B/jZv78gNiqS3399nPUhNI25\nHzhLRNKBM93niEiaiDzlbjMKWCkia4ElwP2qaoWwh95ev48aX8BOcE2jROQZEckRkQ1ex2K8syOv\nnCc/yuSiiSlMHmwD5FpKk7pGiMhM4GEgEnhKVe+vt/7nwDWAD8gFvq+qu1o41rD16OIM1mYV89h3\nJtG7q3WJMA1T1XxgRgPLV+LkJ6r6GTCujUMzh/H6mmyGJMfbbBHmcJ4DHgVe8DgO4xFV5a43NxIT\nFcGv7A5yLeqILcIiEgk8BswCRgOXisjoeputAdJUdTzwGvCHlg40XC3fUcAji9O5aGIK543v63U4\nxpgWlFVYwbIdBVw0IcWu9JhGqepHQIHXcRjvLNqcwwdbc7nxzOH0sgaxFtWUrhGTgQxVzVTVGmAu\nznylX1LVJapa4T5dCvRv2TDDU2F5DTfMXcOA7p2558KxXodjjGlhb3yxF4ALrVuEaQE25WHHVFXr\n5643NzKidxeuOHmQ1+F0OE0phFOAPUHPs9xljbkaeLuhFZakTRcIKDe9upa8smoeuXQSXWJtgg9j\nOpJAQPn3ij1MGdyd1O6dvQ7HdACq+qSqpqlqWnJystfhmBby+AfbySqs5M7ZY4i2AXItrkXfURH5\nLpAG/LGh9ZakTffokgwWb8nhN+eNZpz1HTSmw/koPZfdBRVcdpJNgWSMadju/Aoe/3A7Fxzfj5OH\n9vQ6nA6pKc2M2UBq0PP+7rKDiMiZOJP6f01Vq1smvPC0ZEsODy7axkUTU2yeQGM6qJeW7aZHfAwz\nx9hNNIwxDbt7/kaiI4TbzrWbaLWWprQIrwCGi8hgEYkBLsGZr/RLIjIR+DswW1UbnMTfNE36gVJ+\n+vIaRvXpyn0X2VRpxnREe4sqeX/zAb51YioxUXap0xyeiLwMfA4cJyJZInK11zGZ1vf+5gMs2pzD\nT2cMp0+iDZBrLUdsEVZVn4hcD7yLM33aM6q6UUTuBlaq6jycrhBdgFfdwm23qs5uxbg7pPyyaq5+\nfiWx0ZE8dUUanWIivQ7JGNMK/rl0Fwp8Z/IAr0Mx7YCqXup1DKZtOQPkNjE0OZ6rpg32OpwOrUkj\nsFR1AbCg3rLbgx43ettX0zQVNT6+//xKDpRU8fJ1J9EvqZPXIRljWkFpVS0vLt3FrLF9bJCcMaZB\nf/8wk90FFbx0zRS7atTK7N0NAbX+AD/51xrWZxXx10snMmlAN69DMsa0kn8t201plY8ffG2o16EY\nY0LQnoIK/vZBBueN68u0YTZArrXZnFwe8weUm19dy/tbcrhnzhjOsYEzxnRYVbV+nvpkB6cM68n4\n/kleh2OMCUF3z99EhAi3nWcD5NqCtQh7KBBQfv3f9bzxxV5umXkc35s6yOuQjDGtaO7y3eSWVvPD\n06w12BhzqCVbc1i46QA/mTHMuki2EWsR9og/oPzyP+t4bVUWPzljGD86bZjXIRljWlFpVS1/XZzB\nyUN7cPLQHl6HY4wJMdU+P3fN28iQ5HiuOWWI1+GEDSuEPVDjC3DTq2t5c+1ebjxzODfMGO51SMaY\nVvaPj3dQUF7DL2eOtGkRjTGH+MdHmezMr+CfV0+2AXJtyArhNlZW7eOHL67i4/Q8bp010gbMGBMG\n9hVX8tTHmZw3ri/Hp1rfYGPMwbIKK3h0SQazxvbh1OF25922ZIVwG8ouquSa51ey7UApf/jGeL6V\nlnrkFxlj2r07520koMovZ470OhRjTAj63fzNCMJvzh/tdShhxwrhNrJyZwE/eHE11bV+nrnyRL42\nws74jAkH723cz7sbD/DLmSMZ0MPmDTbGHOyjbbm8s3E/vzjnOFJsgFybs0K4lakqz366k/sWbCal\nWydevnYKw3sneB2WMaYNFJTX8Ns3NjCyTwLXnGp3hzLGHKza5+fOeRsZ3DPePiM8YoVwK8ovq+aX\n/1nHos05nDmqN3/+1vEkdor2OixjTBtQdeYILyyv5ekrTiQ60ga/GGMO9vQnO8jMK+e5q04kNirS\n63DCkhXCreSdDfv5zf82UFJZy+3nj+bKkwcREWEjxY0JF//4OJPFW3K4a/YYxqYkeh2OMSbE7C2q\n5JH3Mzh7dG9OO66X1+GELSuEW9j+4irumb+Jt9bvY3Tfrvzz6smM6tvV67CMMW3o3Y37+f3bW5g1\ntg+XTx3odTjGmBB071ubCajyWxsg5ykrhFtIVa2fZz/dyaOL0/EFlJvPHsH/fW2oXQ41Jsys2lXI\nDXPXcHz/JP7yrQk2Z7Ax5hCfpOfx1vp9/PysEaR2t0G0XrJC+Bj5/AFeX5PNQ4vSyS6q5MxRvbj9\n/DE2OtyYMLR8RwFXPbuc3l3jeOqKNDrFWJ8/Y8zBanwB7pi3gQHdO3PddLuDnNesEG6mGl+A/63J\n5vEPt7Mjr5yxKV35wzfGM21YT69DM8Z4YOGmA/z05TX0S4rjpWtOomeXWK9DMsaEoGc/3cH23HKe\nviKNuGg7WfaaFcJHKa+smrnLd/PPpbs4UFLN6L5deeK7J3DOmN52CdSYMOQPKI8tyeAvC7cxLiWR\nZ648keQEK4KNMYfaX1zFw++nM2NkL2aM6u11OAYrhJvE5w/wcXoer63K4r1N+6n1K6cO78kDF4/n\nayOSrQA2Jkztyi/nF6+uY/nOAi6c0I/7Lx5vLTzGmEbdu2AzvoByxwVjvA7FuKwQbkStP8DyHQUs\nWL+PdzfuJ6+shm6do7l86iAunZzKsF52UwxjwlVZtY/HP8jgqY93EBMVwZ++eTwXT0qxk2JjTKM+\n257Hm2v38tMZw20cUQixQjhIVmEFn2bk8dG2PD7alktptY9O0ZGcMbIXcyb047TjehETZbNAGBOu\nckqrePHzXTz/+S6KK2u5cEI/fjlrJH0T7baoxpjG1foD3PHGRvp368SPThvqdTgmSNgWwjW+AFv2\nl7B2TxGrdxexfEcB2UWVAPTuGsu54/oyY1QvTh2ebCO/jQljpVW1LN6Sw5tr97Fkaw7+gHL26N78\n+PRhHJ+a5HV4xph24PnPdpKeU8Y/LrcBcqGmwxfC/oCSXVjJ9twy0nNK2bq/jM37SsjIKaPGHwAg\nOSGWtIHduG76EKYO7cHwXl3sEqdpd0Tkm8CdwChgsqqubGS7mcDDQCTwlKre32ZBtgP5ZdWsyy5m\nza5CPs/M54s9RdT6lV4JsVxz6mC+nZbKkOQuXodpwozlbfuVU1LFQ4vSOf24ZM4cZXeQCzVNKoSP\nlIAiEgu8AJwA5APfVtWdLRvqoVSVsmofuaXVHCip5kBJFXuLK8kurCSrsJI9hRXsKaig1q9fviY5\nIZZRfbty6oiejE9JYnz/RPp362SFr+kINgBfB/7e2AYiEgk8BpwFZAErRGSeqm5qmxC9FQgoxZW1\n5JfXkFtaTU5pFdlFlewpqGRnXjkZuWXkllYDECEwpl8i3z9lMGeN6s2kAd3sNunGE+Get+3dfQs2\nO3MHXzDGao0QdMRCuIkJeDVQqKrDROQS4AHg28cS2GcZeewqqKC0qpaSSh8lVbUUVdRSWFHj/JTX\nkldWTbUvcMhrEztFk9q9EyN6JXD26D4M7tmZIcldGJbchW7xMccSljEhS1U3A0f6oJ0MZKhqprvt\nXGAOcExfqDvyylmzu9CNIyimr2I76HndA0UJqPMaRd1/nZ0EFAJat17xBxS/KoGA4gs4z30BpdYX\nwBdQavwBanwBqn0Bqmr9VNX6qajxU17to6zaR0llLaXVvoPiq9OtczSDesYzfXgyo/omMLpfV8b3\nT6JLbIe/aGbahxbN2xpfgPnr9rZgeKYx+WU1/O+LvfzkjGEM6hnvdTimAU35lG9KAs7BuSQL8Brw\nqIiIakNfOU3z+Ifb+Tg9D3BaZrp2iiapUzSJnWNI7hLLiN4J9OwSS4/4GHp1jaV3Qhy9E+PomxhH\n5xj78jKmESnAnqDnWcCUhjYUkeuA6wAGDBhw2J0uzcznV/9d30IhNl10pBAdGUFUhBATFUFsVKT7\nbwRx0ZHEx0bSPb4zCbFRdImLIrFTNEmdY+gRH0NyQiy9EmLpl9SJeCt4TWhrUt42NWcra/38/JW1\nLRyiaczgnvH86LRhXodhGtGUT/+mJOCX26iqT0SKgR5AXvBGR/PF+odvjAegS2wU8TFRdknSGEBE\nFgF9Glh1m6q+0ZLHUtUngScB0tLSDntSe/74vpw8tMdXcfJVvtZvoK57XtdyHSHO9iI4rxKIEGcP\nESLO4wiIdB9HRYrz2D4TjDlIU3O2S2wUH/7itLYKK+z17hpnA+RCWJs2gxzNF6tNR2TMoVT1zGPc\nRTaQGvS8v7vsmCTERZMQF32suzHGNKxF8zYyQhjYwy7TGwPQlElxm5KAX24jIlFAIs6gOWNMaFkB\nDBeRwSISA1wCzPM4JmPM4VneGtNKmlIINyUB5wFXuI+/ASw+lv7BxpijJyIXiUgWMBV4S0TedZf3\nE5EF4HRdAq4H3gU2A6+o6kavYjbGHJnlrTGt54hdI9w+v3UJGAk8o6obReRuYKWqzgOeBv4pIhlA\nAU6xbIxpQ6r6OvB6A8v3AucGPV8ALGjD0Iwxx8jy1pjW0aQ+wg0loKreHvS4Cvhmy4ZmjDHGGGNM\n62lK1whjjDHGGGM6HPGqK6+I5AK72uhwPak3lZvHLJ4jC7WY2iKegaqa3MrHaJYwz1cIvZgsnsML\n63yFsM9Zi+fwwjWeBnPWs0K4LYnISlVN8zqOOhbPkYVaTKEWT0cWiu91qMVk8RxeqMXT0YXa+23x\nHJ7FczDrGmGMMcYYY8KSFcLGGGOMMSYshUsh/KTXAdRj8RxZqMUUavF0ZKH4XodaTBbP4YVaPB1d\nqL3fFs/hWTxBwqKPsDHGGGOMMfWFS4uwMcYYY4wxBwm7QlhEbhIRFZGeHsfxRxHZIiLrROR1EUny\nKI6ZIrJVRDJE5FYvYgiKJVVElojIJhHZKCI3eBlPHRGJFJE1IjLf61jCjeXrIXGETL668YRczlq+\nesty9pA4QiZnQzFfwfucDatCWERSgbOB3V7HAiwExqrqeGAb8Ku2DkBEIoHHgFnAaOBSERnd1nEE\n8QE3qepo4CTgxx7HU+cGYLPXQYQby9eDhWC+QmjmrOWrRyxnDxaCORuK+Qoe52xYFcLAg8AtgOcd\no1X1PVX1uU+XAv09CGMykKGqmapaA8wF5ngQBwCquk9VV7uPS3ESI8WreABEpD9wHvCUl3GEKcvX\ng4VUvkLo5azlq+csZw8WUjkbavkKoZGzYVMIi8gcIFtV13odSwO+D7ztwXFTgD1Bz7PwOCnqiMgg\nYCKwzNtIeAjngz3gcRxhxfK1QSGbrxAyOWv56hHL2QaFbM6GSL5CCORslFcHbg0isgjo08Cq24Bf\n41yyCYl4VPUNd5vbcC5XvNSWsYUyEekC/Ae4UVVLPIzjfCBHVVeJyGlexdFRWb52HKGQs5avrc9y\ntmMIhXx14wiJnO1QhbCqntnQchEZBwwG1ooIOJdIVovIZFXd39bxBMV1JXA+MEO9mccuG0gNet7f\nXeYZEYnGSdCXVPW/XsYCTANmi8i5QBzQVUReVNXvehxXh2D5etRCLl8hpHLW8rWVWc4etZDL2RDK\nVwiRnA3LeYRFZCeQpqp5HsYwE/gL8DVVzfUohiicQQQzcJJzBfAdVd3oUTwCPA8UqOqNXsTQGPds\n9WZVPd/rWMKN5euXIhSqdAAAALhJREFUMYRUvroxhWTOWr56y3L2yxhCKmdDNV/B25wNmz7CIehR\nIAFYKCJfiMgTbR2AO5DgeuBdnE7zr3j5pYpzdvg94Az3PfnCPVM0xmuWrw2znDWhynL2UJavDQjL\nFmFjjDHGGGOsRdgYY4wxxoQlK4SNMcYYY0xYskLYGGOMMcaEJSuEjTHGGGNMWLJC2BhjjDHGhCUr\nhI0xxhhj/r/dOhAAAAAAEORvPchFEUsiDADAkggDALAU/VPqZhdgDPgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x216 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_yo9fwwciDY",
        "colab_type": "text"
      },
      "source": [
        "The ReLU activation function ($max(0,z)$) is by far the most widely used activation function for neural networks. But as you can see, each activation function has its own constraints so there are circumstances where you'll want to use different ones. For example, if we need to constrain our outputs between 0 and 1, then the sigmoid activation is the best choice."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u72au7H9oHk7",
        "colab_type": "text"
      },
      "source": [
        "<img width=\"45\" src=\"http://bestanimations.com/HomeOffice/Lights/Bulbs/animated-light-bulb-gif-29.gif\" align=\"left\" vspace=\"20px\" hspace=\"10px\">\n",
        "\n",
        "In some cases, using a ReLU activation function may not be sufficient. For instance, when the outputs from our neurons are mostly negative, the activation function will produce zeros. This effectively creates a \"dying ReLU\" and a recovery is unlikely. To mitigate this effect, we could lower the learning rate or use [alternative ReLU activations](https://medium.com/tinymind/a-practical-guide-to-relu-b83ca804f1f7), ex. leaky ReLU or parametric ReLU (PReLU), which have a small slope for negative neuron outputs. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xqWpi0X2dWpe",
        "colab_type": "text"
      },
      "source": [
        "# From scratch\n",
        "\n",
        "Now let's create our multilayer perceptron (MLP) which is going to be exactly like the logistic regression model but with the activation function to map the non-linearity in our data. Before we use TensorFlow 2.0 + Keras we will implement our neural network from scratch using NumPy so we can:\n",
        "1. Absorb the fundamental concepts by implementing from scratch\n",
        "2. Appreciate the level of abstraction TensorFlow provides\n",
        "\n",
        "<div align=\"left\">\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/lightbulb.gif\" width=\"45px\" align=\"left\" hspace=\"10px\">\n",
        "</div> \n",
        "\n",
        "It's normal to find the math and code in this section slightly complex. You can still read each of the steps to build intuition for when we implement this using TensorFlow + Keras.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5H7liW_Adr2n",
        "colab_type": "code",
        "outputId": "fca6fd7c-a16f-425a-bf82-0ecb5b47c22c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print (f\"X: {standardized_X_train.shape}\")\n",
        "print (f\"y: {y_train.shape}\")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X: (1083, 2)\n",
            "y: (1083,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0prEPVOlv1F4",
        "colab_type": "text"
      },
      "source": [
        "Our goal is to learn a model  𝑦̂   that models  𝑦  given  𝑋 . You'll notice that neural networks are just extensions of the generalized linear methods we've seen so far but with non-linear activation functions since our data will be highly non-linear.\n",
        "\n",
        "$z_1 = XW_1$\n",
        "\n",
        "$a_1 = f(z_1)$\n",
        "\n",
        "$z_2 = a_1W_2$\n",
        "\n",
        "$\\hat{y} = softmax(z_2)$ # classification\n",
        "\n",
        "* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n",
        "* $W_1$ = 1st layer weights | $\\in \\mathbb{R}^{DXH}$ ($H$ is the number of hidden units in layer 1)\n",
        "* $z_1$ = outputs from first layer  $\\in \\mathbb{R}^{NXH}$\n",
        "* $f$ = non-linear activation function\n",
        "* $a_1$ = activation applied first layer's outputs | $\\in \\mathbb{R}^{NXH}$\n",
        "* $W_2$ = 2nd layer weights | $\\in \\mathbb{R}^{HXC}$ ($C$ is the number of classes)\n",
        "* $z_2$ = outputs from second layer  $\\in \\mathbb{R}^{NXH}$\n",
        "* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NXC}$ ($N$ is the number of samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a25Zg2yyvhKL",
        "colab_type": "text"
      },
      "source": [
        "1. Randomly initialize the model's weights $W$ (we'll cover more effective initialization strategies later in this lesson)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3udiI1Idr62",
        "colab_type": "code",
        "outputId": "f1474861-ecae-4a80-a343-f014c7490660",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Initialize first layer's weights\n",
        "W1 = 0.01 * np.random.randn(INPUT_DIM, HIDDEN_DIM)\n",
        "b1 = np.zeros((1, HIDDEN_DIM))\n",
        "print (f\"W1: {W1.shape}\")\n",
        "print (f\"b1: {b1.shape}\")"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W1: (2, 100)\n",
            "b1: (1, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqllhIMgxGq7",
        "colab_type": "text"
      },
      "source": [
        "2. Feed inputs $X$ into the model to do the forward pass and receive the probabilities."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pH9-D8YE4pHH",
        "colab_type": "text"
      },
      "source": [
        "First we pass the inputs into the first layer.\n",
        "  * $z_1 = XW_1$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAeYeoMtdr-t",
        "colab_type": "code",
        "outputId": "976c193f-4968-4c9e-a879-261212641aae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# z1 = [NX2] · [2X100] + [1X100] = [NX100]\n",
        "z1 = np.dot(standardized_X_train, W1) + b1\n",
        "print (f\"z1: {z1.shape}\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "z1: (1083, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15yZEt903YOO",
        "colab_type": "text"
      },
      "source": [
        "Next we apply the non-linear activation function, ReLU ($max(0,z)$) in this case.\n",
        "  * $a_1 = f(z_1)$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAFau-Zm3YVM",
        "colab_type": "code",
        "outputId": "75c8a548-26a0-4f31-b15e-83d9da2f6f13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Apply activation function\n",
        "a1 = np.maximum(0, z1) # ReLU\n",
        "print (f\"a_1: {a1.shape}\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a_1: (1083, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNHCPVa03YGB",
        "colab_type": "text"
      },
      "source": [
        "We pass the activations to the second layer to get our logits.\n",
        "  * $z_2 = a_1W_2$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyJS1y2o3YJb",
        "colab_type": "code",
        "outputId": "769e82d7-a599-49c7-a884-181594306372",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Initialize second layer's weights\n",
        "W2 = 0.01 * np.random.randn(HIDDEN_DIM, NUM_CLASSES)\n",
        "b2 = np.zeros((1, NUM_CLASSES))\n",
        "print (f\"W2: {W2.shape}\")\n",
        "print (f\"b2: {b2.shape}\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W2: (100, 3)\n",
            "b2: (1, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_sCtbVh5DfI",
        "colab_type": "code",
        "outputId": "705486e5-09b3-47dc-d091-8f469042a27e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# z2 = logits = [NX100] · [100X3] + [1X3] = [NX3]\n",
        "logits = np.dot(a1, W2) + b2\n",
        "print (f\"logits: {logits.shape}\")\n",
        "print (f\"sample: {logits[0]}\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "logits: (1083, 3)\n",
            "sample: [ 0.0004134   0.0002782  -0.00118021]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6r_NhZuP3X5g",
        "colab_type": "text"
      },
      "source": [
        "We'll apply the softmax function to normalize the logits and btain class probabilities.\n",
        "  * $\\hat{y} = softmax(z_2)$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wI3jpHGJ3X_i",
        "colab_type": "code",
        "outputId": "0aef6c54-614b-47ac-9ff8-63b100e21b4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Normalization via softmax to obtain class probabilities\n",
        "exp_logits = np.exp(logits)\n",
        "y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n",
        "print (f\"y_hat: {y_hat.shape}\")\n",
        "print (f\"sample: {y_hat[0]}\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "y_hat: (1083, 3)\n",
            "sample: [0.33352539 0.3334803  0.33299431]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VzjdcZG2h_h",
        "colab_type": "text"
      },
      "source": [
        "3. Compare the predictions $\\hat{y}$ (ex.  [0.3, 0.3, 0.4]]) with the actual target values $y$ (ex. class 2 would look like [0, 0, 1]) with the objective (cost) function to determine loss $J$. A common objective function for classification tasks is cross-entropy loss. \n",
        "  * $J(\\theta) = - \\sum_i ln(\\hat{y_i}) = - \\sum_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) $"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0llchiDhxGGI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loss\n",
        "correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n",
        "loss = np.sum(correct_class_logprobs) / len(y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6y4ZWFp32jd2",
        "colab_type": "text"
      },
      "source": [
        "4. Calculate the gradient of loss $J(\\theta)$ w.r.t to the model weights. \n",
        "  \n",
        "  The gradient of the loss w.r.t to W2 is the same as the gradients from logistic regression since $\\hat{y} = softmax(z_2)$.\n",
        " * $\\frac{\\partial{J}}{\\partial{W_{2j}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}0 - e^{a_1W_{2y}}e^{a_1W_{2j}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{a_1e^{a_1W_{2j}}}{\\sum_j e^{a_1W}} = a_1\\hat{y}$\n",
        "  * $\\frac{\\partial{J}}{\\partial{W_{2y}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}a_1 - e^{a_1W_{2y}}e^{a_1W_{2y}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{1}{\\hat{y}}(a_1\\hat{y} - a_1\\hat{y}^2) = a_1(\\hat{y}-1)$\n",
        "\n",
        "  The gradient of the loss w.r.t W1 is a bit trickier since we have to backpropagate through two sets of weights.\n",
        "  * $ \\frac{\\partial{J}}{\\partial{W_1}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}} \\frac{\\partial{\\hat{y}}}{\\partial{a_1}}  \\frac{\\partial{a_1}}{\\partial{z_1}}  \\frac{\\partial{z_1}}{\\partial{W_1}}  = W_2(\\partial{scores})(\\partial{ReLU})X $"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9D2ujwNOxGQ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dJ/dW2\n",
        "dscores = y_hat\n",
        "dscores[range(len(y_hat)), y_train] -= 1\n",
        "dscores /= len(y_train)\n",
        "dW2 = np.dot(a1.T, dscores)\n",
        "db2 = np.sum(dscores, axis=0, keepdims=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEpKpAgn78h4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dJ/dW1\n",
        "dhidden = np.dot(dscores, W2.T)\n",
        "dhidden[a1 <= 0] = 0 # ReLu backprop\n",
        "dW1 = np.dot(standardized_X_train.T, dhidden)\n",
        "db1 = np.sum(dhidden, axis=0, keepdims=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1j-lgGq2lb_",
        "colab_type": "text"
      },
      "source": [
        "5. Update the weights $W$ using a small learning rate $\\alpha$. The updates will penalize the probabiltiy for the incorrect classes (j) and encourage a higher probability for the correct class (y).\n",
        "  * $W_i = W_i - \\alpha\\frac{\\partial{J}}{\\partial{W_i}}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kZoWyx-xGW8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Update weights\n",
        "W1 += -LEARNING_RATE * dW1\n",
        "b1 += -LEARNING_RATE * db1\n",
        "W2 += -LEARNING_RATE * dW2\n",
        "b2 += -LEARNING_RATE * db2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6lho6k572mge",
        "colab_type": "text"
      },
      "source": [
        "6. Repeat steps 2 - 4 until model performs well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScHz7h6OxGU_",
        "colab_type": "code",
        "outputId": "0d32ac44-52fb-4b68-a960-ecfa8fcd96d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# Initialize random weights\n",
        "W1 = 0.01 * np.random.randn(INPUT_DIM, HIDDEN_DIM)\n",
        "b1 = np.zeros((1, HIDDEN_DIM))\n",
        "W2 = 0.01 * np.random.randn(HIDDEN_DIM, NUM_CLASSES)\n",
        "b2 = np.zeros((1, NUM_CLASSES))\n",
        "\n",
        "# Training loop\n",
        "for epoch_num in range(1000):\n",
        "\n",
        "    # First layer forward pass [NX2] · [2X100] = [NX100]\n",
        "    z1 = np.dot(standardized_X_train, W1) + b1\n",
        "\n",
        "    # Apply activation function\n",
        "    a1 = np.maximum(0, z1) # ReLU\n",
        "\n",
        "    # z2 = logits = [NX100] · [100X3] = [NX3]\n",
        "    logits = np.dot(a1, W2) + b2\n",
        "    \n",
        "    # Normalization via softmax to obtain class probabilities\n",
        "    exp_logits = np.exp(logits)\n",
        "    y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n",
        "\n",
        "    # Loss\n",
        "    correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n",
        "    loss = np.sum(correct_class_logprobs) / len(y_train)\n",
        "\n",
        "    # show progress\n",
        "    if epoch_num%100 == 0:\n",
        "        # Accuracy\n",
        "        y_pred = np.argmax(logits, axis=1)\n",
        "        accuracy =  np.mean(np.equal(y_train, y_pred))\n",
        "        print (f\"Epoch: {epoch_num}, loss: {loss:.3f}, accuracy: {accuracy:.3f}\")\n",
        "\n",
        "    # dJ/dW2\n",
        "    dscores = y_hat\n",
        "    dscores[range(len(y_hat)), y_train] -= 1\n",
        "    dscores /= len(y_train)\n",
        "    dW2 = np.dot(a1.T, dscores)\n",
        "    db2 = np.sum(dscores, axis=0, keepdims=True)\n",
        "\n",
        "    # dJ/dW1\n",
        "    dhidden = np.dot(dscores, W2.T)\n",
        "    dhidden[a1 <= 0] = 0 # ReLu backprop\n",
        "    dW1 = np.dot(standardized_X_train.T, dhidden)\n",
        "    db1 = np.sum(dhidden, axis=0, keepdims=True)\n",
        "\n",
        "    # Update weights\n",
        "    W1 += -1e0 * dW1\n",
        "    b1 += -1e0 * db1\n",
        "    W2 += -1e0 * dW2\n",
        "    b2 += -1e0 * db2"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0, loss: 1.099, accuracy: 0.243\n",
            "Epoch: 100, loss: 0.551, accuracy: 0.680\n",
            "Epoch: 200, loss: 0.223, accuracy: 0.912\n",
            "Epoch: 300, loss: 0.131, accuracy: 0.951\n",
            "Epoch: 400, loss: 0.090, accuracy: 0.976\n",
            "Epoch: 500, loss: 0.069, accuracy: 0.985\n",
            "Epoch: 600, loss: 0.056, accuracy: 0.994\n",
            "Epoch: 700, loss: 0.048, accuracy: 0.996\n",
            "Epoch: 800, loss: 0.043, accuracy: 0.997\n",
            "Epoch: 900, loss: 0.039, accuracy: 0.997\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hDGiO6Ng8CI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MLPFromScratch():\n",
        "    def predict(self, x):\n",
        "        z1 = np.dot(x, W1) + b1\n",
        "        a1 = np.maximum(0, z1)\n",
        "        logits = np.dot(a1, W2) + b2\n",
        "        exp_logits = np.exp(logits)\n",
        "        y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n",
        "        return y_hat"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dv5Y3-1ibuPM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Evaluation\n",
        "model = MLPFromScratch()\n",
        "logits_train = model.predict(standardized_X_train)\n",
        "pred_train = np.argmax(logits_train, axis=1)\n",
        "logits_test = model.predict(standardized_X_test)\n",
        "pred_test = np.argmax(logits_test, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XfmYidNSgkcv",
        "colab_type": "code",
        "outputId": "53027197-a1af-4590-a7db-267509a7e10e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Training and test accuracy\n",
        "train_acc =  np.mean(np.equal(y_train, pred_train))\n",
        "test_acc = np.mean(np.equal(y_test, pred_test))\n",
        "print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 1.00, test acc: 1.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5pDl4hdgkaE",
        "colab_type": "code",
        "outputId": "8408f2d4-6372-437c-f2b8-fc985e5b1141",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rFOH1OCYuDBeCQ66AA7rFOdLhseH0s+PIpWSkIvm6JEJgd0ncUq0KvS+CFZUV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\nlFJKKaXqjgbBSimllFKq7vx/oDRSBLBSbLcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kV2rzKCLdSEu",
        "colab_type": "text"
      },
      "source": [
        "Credit for the plotting functions and the intuition behind all this is due to [CS231n](http://cs231n.github.io/neural-networks-case-study/), one of the best courses for machine learning. Now let's implement the MLP with TensorFlow + Keras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "br_O_NUCjkwm",
        "colab_type": "text"
      },
      "source": [
        "# TensorFlow + Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjpH19aiWNFe",
        "colab_type": "text"
      },
      "source": [
        "### Components"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-U0_3Xco0Q4g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# MLP\n",
        "class MLP(Model):\n",
        "    def __init__(self, hidden_dim, num_classes):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc1 = Dense(units=hidden_dim, activation='relu') # replaced linear with relu\n",
        "        self.fc2 = Dense(units=num_classes, activation='softmax')\n",
        "        \n",
        "    def call(self, x_in, training=False):\n",
        "        \"\"\"Forward pass.\"\"\"\n",
        "        z = self.fc1(x_in)\n",
        "        y_pred = self.fc2(z)\n",
        "        return y_pred\n",
        "    \n",
        "    def sample(self, input_shape):\n",
        "        x_in = Input(shape=input_shape)\n",
        "        return Model(inputs=x_in, outputs=self.call(x_in)).summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stBQV5LiWNCb",
        "colab_type": "text"
      },
      "source": [
        "### Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-8S6w1njkDC",
        "colab_type": "code",
        "outputId": "84c6233a-fabd-4d1d-986b-6c29bce9ce9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Initialize the model\n",
        "model = MLP(hidden_dim=HIDDEN_DIM,\n",
        "            num_classes=NUM_CLASSES)\n",
        "model.sample(input_shape=(INPUT_DIM,))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 2)]               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 100)               300       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 3)                 303       \n",
            "=================================================================\n",
            "Total params: 603\n",
            "Trainable params: 603\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtZvQVVpvl5p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Compile\n",
        "optimizer = Adam(lr=LEARNING_RATE)\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss=SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nmeU45XF0O-U",
        "outputId": "d1b34081-9987-46fd-a19d-61ab136b9386",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Training\n",
        "model.fit(x=standardized_X_train, \n",
        "          y=y_train,\n",
        "          validation_data=(standardized_X_val, y_val),\n",
        "          epochs=NUM_EPOCHS,\n",
        "          batch_size=BATCH_SIZE,\n",
        "          class_weight=class_weights,\n",
        "          shuffle=False,\n",
        "          verbose=1)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1083 samples, validate on 192 samples\n",
            "Epoch 1/10\n",
            "1083/1083 [==============================] - 1s 769us/sample - loss: 0.0021 - accuracy: 0.5540 - val_loss: 0.0017 - val_accuracy: 0.5833\n",
            "Epoch 2/10\n",
            "1083/1083 [==============================] - 0s 147us/sample - loss: 0.0014 - accuracy: 0.7470 - val_loss: 0.0012 - val_accuracy: 0.8333\n",
            "Epoch 3/10\n",
            "1083/1083 [==============================] - 0s 137us/sample - loss: 8.5693e-04 - accuracy: 0.9049 - val_loss: 7.6481e-04 - val_accuracy: 0.8906\n",
            "Epoch 4/10\n",
            "1083/1083 [==============================] - 0s 137us/sample - loss: 5.4156e-04 - accuracy: 0.9501 - val_loss: 5.5680e-04 - val_accuracy: 0.9531\n",
            "Epoch 5/10\n",
            "1083/1083 [==============================] - 0s 140us/sample - loss: 3.8934e-04 - accuracy: 0.9649 - val_loss: 4.4130e-04 - val_accuracy: 0.9635\n",
            "Epoch 6/10\n",
            "1083/1083 [==============================] - 0s 145us/sample - loss: 3.0538e-04 - accuracy: 0.9741 - val_loss: 3.6685e-04 - val_accuracy: 0.9635\n",
            "Epoch 7/10\n",
            "1083/1083 [==============================] - 0s 138us/sample - loss: 2.5231e-04 - accuracy: 0.9778 - val_loss: 3.0834e-04 - val_accuracy: 0.9792\n",
            "Epoch 8/10\n",
            "1083/1083 [==============================] - 0s 145us/sample - loss: 2.1546e-04 - accuracy: 0.9815 - val_loss: 2.6535e-04 - val_accuracy: 0.9948\n",
            "Epoch 9/10\n",
            "1083/1083 [==============================] - 0s 138us/sample - loss: 1.8842e-04 - accuracy: 0.9834 - val_loss: 2.3150e-04 - val_accuracy: 0.9948\n",
            "Epoch 10/10\n",
            "1083/1083 [==============================] - 0s 144us/sample - loss: 1.6813e-04 - accuracy: 0.9843 - val_loss: 2.0421e-04 - val_accuracy: 0.9948\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1d67d6eba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4tI2iZ1vl8e",
        "colab_type": "code",
        "outputId": "b8a1d12a-8d9f-4500-96a3-21a5cb3a6e42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Predictions\n",
        "pred_train = model.predict(standardized_X_train) \n",
        "pred_test = model.predict(standardized_X_test)\n",
        "print (f\"sample probability: {pred_test[0]}\")\n",
        "pred_train = np.argmax(pred_train, axis=1)\n",
        "pred_test = np.argmax(pred_test, axis=1)\n",
        "print (f\"sample class: {pred_test[0]}\")"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample probability: [1.2431189e-03 5.1021564e-04 9.9824667e-01]\n",
            "sample class: 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ieu55J1Gc7IK",
        "colab_type": "code",
        "outputId": "9ae74a48-6586-4d65-d17f-327e7cb6a514",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Accuracy\n",
        "train_acc = accuracy_score(y_train, pred_train)\n",
        "test_acc = accuracy_score(y_test, pred_test)\n",
        "print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 0.99, test acc: 0.99\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgM4qu8I62BP",
        "colab_type": "code",
        "outputId": "d8e8fd56-ef28-4f05-9e98-4335ed284c3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "# Metrics\n",
        "plot_confusion_matrix(y_test, pred_test, classes=classes)\n",
        "print (classification_report(y_test, pred_test))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vh2t/+aCy59yHKyphYeEcPHjAtR4ff5Dw8PAc6YKDg0lLSwWgWrVqnDp5koyMDEeegwcJ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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99        75\n",
            "           1       1.00      0.99      0.99        75\n",
            "           2       0.99      1.00      0.99        75\n",
            "\n",
            "    accuracy                           0.99       225\n",
            "   macro avg       0.99      0.99      0.99       225\n",
            "weighted avg       0.99      0.99      0.99       225\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xj7cwzZ4NoVI",
        "colab_type": "code",
        "outputId": "d47fb2ab-16a1-42f5-899a-230fadbc0cb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OEMk2xTwanBVtkmuRCKe5X29RsRtg3ZnHO+CC2+9EYJlX7dpdNvQMebD9KAiw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FZF+JI/yIXHYvwB8boZsNCcatmWsaoptPY30trfSHQlTiKSwu134gttrUWtypTxduzwSqHwwplK\n2TuG7ALJtfCWA3GFkLrB3NWbzF+9RfPxAQLdHayUcHsQQqGhv5u1ieninewhrnx7hIu/HN53/+Cu\nE60s3l8jnShNiKRhkoikrc9tGZlMIpxkeXKdlv79sSA6RBkUWBUWIft3TNicQ/Gk1ZTIJzKJZFVJ\nsBV+FON3vxXlj77ve+D0khu+v/kaarlzD+BqYSuiW2M+0k3DR/C1tbA2Po2ZTuPvbMPX3oIQguj8\nIlNvfwRSWg4/JSClZH1yJkeaSyE02EdkZoH48qpVVBECIQTtZ07iqIIryqcB9UeCy0FT7VvypRwi\nsnC7rC/TtDyHTdNaRAtPumxFWckMWric1oKbdYiIxir2pdwIz0gyclnUBRGuFL6QpyLyC1W02cne\nyOyUCJvmhv9wOl3xTU134/b8g03dQGQ8mEtCEaXbtoaJBJZujtr6SDoCXoxkClHmd+DvbKv4WLeD\nwiCN/fg8OzKWf1f/012S0eKKiVAVvI0uXD5nWbGGaUhWpg5J8IFjK6vJ3BxGvPw6m0huWGTuETHK\nJ8I/GtUYufxgEOHC9XgDB0yAweoOlCpiFcxu1BJcQT9tDx3b9JgeTzD55odbdgKlYaJvEcQlFIXe\nz1wgtrBMZHYB1akR7O3C6avu30p/80Pks89xELK3vUZ9ySHKQckEWlRCgrLbFEomFGXDaquM7ypu\n1wZJzgrgFbGRKLcNfG1IHohn8EFjOrrOZGSF4UuJ6vhM2lWGKkH2b+90bF1tyMNO9GPuxqCth6Mr\nGKDvM4/hbmq0bXHZEWCA5Mo6oz97ncTKmu02qfDetbRWX1rgUnqN4UtbJyhWCw6XxvCTvUXDmUKA\ny+ugod2PJ+CiqbsMIRegHg7G1QdiWxDgfOxxZdAiwhv+wZORlbqTR0xGVjZ9FRYk8r8OHHZrs8gL\nUKoDrN6fquizKTQVd4mEuaLthMDX1kz7IydoOTFcdQIM1tqe9Q+ejCzX3ee8HB6cSnA5h4dSsDuZ\npLS8Jst9RkvZpgmxEZyxB230Bwl7YrOTderweDZPgW83NtvpsL7yZS+lNvd3Amw7UU7RVNoePs78\nJzeRedVmoaq0nz2FtznEkUtPEpldYPqdK5jbnXregiCEp2Zo6Nt9hHItIdDi5djFPsbenyaVCcpo\n7PQz+Fh3rio+/EQPmlNlbqQ42ENRrBjYQxwwnGXW8KwUrca029mKMOj/P3tvGiJJlp5rvsfMfHcP\n99j3NfetqjKzuvbqqq5eJLXuKujLMFy4IIb+NaBB0Iyg//QMNAga7lxxmR9qxIUBMcwgqB6JK13d\nVquqeqm9cqvcMyMjY9893MP3xczO/Dh+3M3dzcz3NeyByIxwNzM/vh377Dvf9749pR9cNAef0ppH\nKa2XPasXRdVf7eviTLAeciJpmswAABACm8sF70R3mPdo9YN/9O5oz3zOq6F/gmCzmlCjJRSjQFgS\n2XKaUX2xmXEGvwqTZSbLVqkjmSoACNQeUYxoFD2ZHTQu38mQFSAaKw5oa9GY1G5jkwoSe2a7DM7X\nbKQxuDQHu9eD4ONnyCaScAb8GDl/qkjT0e71VJ4o60AwUklpEjzLrdL2ZmYCkz689IdnoWQUplQi\nEIQ2IwjvRmFzMHOMxetTUGUVB6vhon1dficcbssuuaMQGJsi8Ma2KoxjOkGxkUYSyx+5yjJlnQ4W\nSsdTlIQY0s4JXRoAA0A6Xd7gzpvVKzSQ1YJ4/TIC6Qy23m/aIYtwjQzheHMH1CBZRkQBAzOTGLty\n3rS0rRPc6UMjjf4Jgo1qQmt1CeOkM+wLx4NrvnyRSDH1CTPJNYDd7/UwjUODpY/yLEIIAhF7/kNl\nRLHMjtK6LmP+evOSgnrefy6wX8UyGxmcB2I7NRlpeMaG4RkbNrxfcjlARKGpgTARRfjny+V5msVW\neAn46S/xk7/4AX4CYPmjIwhEatvnmRACySFBySq4/08rSMUyzOiFALtPgxhZCOBwPVy2XzyUxNe/\nXMaLv3/G0gvuFJU0fg2UWgyRcr0bitK27HHBSCMJ7al1+SNbR4OFwryrDRTlwhzcndcW5fB6cLez\nMKfz25oE8U6CtsA8Q4tvehyHD54iqxRLvgqShMXvvtX1DW2daoRuFf1TE6xXE2oUAJdaJ5fexwOf\nWIJ9wXijRTTGmqaS6fJ9Sx+L1y/ZzTNMLIsw0Pe+9B2R2alXN7gOuGxas7zoQ8urUJtY50ZEAYGF\nabhHW7vsvxVeQvJP/gY/mznomP/81sMDJCPpgtMhZc1v+89CoHrxEAXktKJbKmHRJpql8SsITCPe\n42JlbV5PYXWuDbzrHcD/9g2Cf7mUzv+cfjfZMTm14sRDWvPTwiREs3HYgQEf+/G4WIIqGme6wdF4\n00shWmKeoT2+IGDk0hnYeA8TAdxjw5j/1muwuZxQMlkEnz7H5mc3sX/3MTJdaMjVT/rB/ZMJ5jWh\nlSR2APOyCQBIaq4sZblck1JR2GM5nebZRl5aUcWVtjaLwJfTejkjXL781oAGcD00GvxyE5ZaHjJn\nwdkMwqtbte8kCCACYbXGmos01+gQxq+chzPQns9Tp+vHDlePdS1LzaAqRXDjGNMXu6MG70SiV4JG\naW1lENxmWXsMSWTHbVM5Rb48IsfL30jgqyUmp9buQLh43u3S84kkslU3IdfglskUenIc9vLPBC9x\na6LsXSk8I9wMzWAtqqJi4+MvkQpF8pJmAMHA9AQcPi8y8QTWPvwUqqKweZwQhFbWMP3q1a6pD+Zw\nRaCX//Rd/Pv/ONCzGeH+CYIBVhMaibEvkyQCTpexZFop3CEskaxOBURWCoYZWrWI0mPWrR/cu4Ew\n7zLW6ky2xWdeC6UF+bpqt9fqQStKU+vMaqWeMojRS2eQPAwhtrtfdHvqKIxsMtW2IJhTWj/WrknS\nSH2jEolwCjf/7hFOvToD/7iO655Fa0nngh+nvVCClkpX/z3kpWt6TcsOe8dqikvl1NpJ2+fdWuFB\nLlDQ3XfYC2WEen05hLDbWxgEt4rQyhpSoeNCU3Rurtq78xDeyTHs3XpQbJBBmbvl9pd3cOYP3wMx\nc7ftAPff9+ESPsJf5wPhI8x4e6vJuL+CYI7NlgtKa9yv1H65WjIZYzebGgOpgn5w7xhpaDHWmWzj\nREwI4HKw97/amnB+0q31xNsivFPjCK+s1/R59IwN4/D+07KLOKqoOLj7GL4WaQSb0Yn6saHZAew/\nO9IvfahAJinj0W9WcerVGYzMWbrBbSeTqb3+l8MVerqrlwhAIRB+2bgNoEV0cQDMg9nSMkKAnU8r\nzcGlBlc9wPHzzSJVIC2RzR3ED4L6O1IgeRSGe6T7Asz77/sw/cHf4K//4gf4yU17z2lm918Q7HbW\nXwuqUpZFdjhYY4WqsqtNPZkqbuupqDmjjCQrxeAQ1KZpqYEHwtoltG4MhE07jjuZffC66zPO4Nu7\nnGz/DmWOVEWBzeWoaR8iCIht7xs+50ydNt/NoNRIo9WB8MzFMYQ2I5AzClSFXREIIsHo4iAOVsOF\nWmEDqAqs3tzB8Ky/67qzLUxQFOMAuAuk1bo2GO0UpUoPHN5LU2n+recqt8OoBvEABWX3GSY9utuk\nYiu8hEt/+xH+xTvv4T991OnR1EZ/BcGCYB4A86xgaXaQZwBVFfDllkEJYYEul0vjSy/clEPQHIfL\n90SiuSU5sHIJnpHWupFVSbdbc2qlzjj/8hTtnM6kTWLvlSA05hynzURIImuObBOUUqQjMWx/cRvZ\nRLKmLDBVVaSOo4YuRJ1eRiuvH2vdshlzkjuD/WdHCG1HITlFTJwehn/ci1Qsg+NdE9v1HKqsIp3I\nwulpbk1gKyGEDAH4fwEsAFgF8O8opWUdK4QQBcDd3J/rlNJ/1a4xthQupaYtTcv3ebTYwMVmY+cL\nRekp44aWIuXmZC5hZtSIbjZXZ7LlxlW8X6O740JdBqbHcfRsrbzRU6VQszJcI0NIHuo16BK4qjDO\nsKid/gqCRdF4OYzX/PIvnaTZVmu0AJQvzzgd7Evnchak0bS1ZzzQzWQLUm0D3uLjqJTVENcQ2HSr\nNWep1Fmh7CHXfNHOBCohrAOcu/Y1yyWK16e1yfwkGQxh+8uvkU1WWZOug5HLXLdQWDb7d/j3/7G1\nGpOSXcTUhVFMXShuJqm2YY5SQJK6+/XU4c8A/DOl9M8JIX+W+/t/1dkuSSl9qb1DazE2ic3PWuks\ngM3HqXQhE8znc67ak5WBVBV67kYIQnEzHk+omEhjngi8nsJqKa/tjSeLV1WzMnvPSuGvmySyixdC\n2PvLz9dZmUmV9iBDZ5cQ2dyBnMqUfT5Cy6tw+LwgkgiolPWFEJbAmHz5SscTGdVBAdCuXb3Wo7+C\nYEqNl8MoZYGuFlEsdpuRTAw3eKBlpALhcBRqmDyu8m0FsFKNGjUN9bSEu4Eird9O6ky6nAXraqD5\nkmhi64PgbCKJ9d99VdFL3gwiCHAGBhDb2dcVYW+F8Ua9eA+OcfpdG1Z+3f7ms+E5P2LBRL5Mwgj3\ngAOSo+emx38N4N3c7/8XgI+gHwT3FzZJXxUoky3PAHs9xfOFTQIkcz13U0rnev6/21V+vjkpOB2F\nABgo/O9xAcca+UhKWTDrdhZvx19Pj5utxCWSyFvbm5YMdD+Sw47Fb7+FrS/uILF/WHQfVVSko3FM\nXL2EbCKB5GEYNq8bg6fm4PB1f6Pu/fd9eOf1MPDH6Cn94J6b5U2RZWN3OL1O0lqCDqMAmMOteo2W\n4wkxNtiogDYQxqnmCYPXTb7kocNXeqJo7DLVLNow4YZW1kEbrG8T7DYMzEyyxjgdtG50J5nRhQD2\nnx0xHWGTQFitNzPYWcYppVyjbxfAuMF2TkLIVwBkAH9OKf3/9DYihPwQwA8BYG6mu+SZitBmgDna\nulL+HbbZigNgvh3Atq1VbUAgJnN9riyuJz9GDVJavsChKHfhzGaBmMqy6aXwBudYoqDY0weIdpth\nwoMqChIHR5i8frnNo2oOrW6EppSCqhREIE3r1+ivIBhgV99chDq/fJKtboIrrSfjVPNi8w91pU3r\nnBi59mTM0Q3ZhQ53HLscTFeyHdShEiG9fhWXcAv3368u8Ewfx+pfjs2hZmXYXE4MzE4hsrld1IFM\nRAFjV843dPxmsRVewojGWrndy2aCKOD8N+dx+x+eAiZBcDLSnTZahJBfAZjQuevH2j8opZQQYvQE\n5ymlW4SQJQAfEELuUkqflW5EKf05gJ8DwMtXz3RvOGfWAyKKhSV4o5U+vtxes+RWiyb7Xsfo/TB6\nuQTBuIxR1HFwtEls/iek+nN7lyEYNQVWuK8XaEUjNFUpNu/vY/dJEIqswuaSMPfCOEYXGg+we/vV\n1kNRmVawlMsQKkplhQaeNZByXzijBjojuKwWryE1QlUbnhNPfIex01GYACtRr2U237eOZhpumFFL\nIOwM+Jg0TgMlC1JOom/i2iXYfR4cLa9CyWTgHPBh9Mo5eEbbrs1kSKf95w/Xj6FWUAsQbd1Zf0cp\n/Y7RfYSQPULIJKV0hxAyCWBfbztK6Vbu/xVCyEcArgIoC4J7HkLYEjzA5me+lK63UljPRajR8QB2\nvB5etm+IrGy8QsdLtSQJzKpRMVd5KH1f3K7iY4sCO3dHO6d+Uw+Di7NIHByVZYSJIMA/P92hUTWP\nZhtpPL+xjcO1cH71LpuU8fyrbVAVGFtqLBDuzpm+Gcgyu0qsFFjwJjano7jpTfu/EVQz0blcbEmH\nC39r7+O/t7pD+SSgl6nXg7/mtMLJqAUnKm6hbHvjWlUWyoGlOQhCY0s7cjqN7S/uIJtIYvjsIs58\n/1s4/29+DwvvvdFVATDnzo+W87abKpXbZrsZ2opg/es90/MuEQkmTnffa1YFfwfgP+R+/w8A/rZ0\nA0LIICHEkft9BMCbAB60bYStIGViY89/nBUkB+vNJiZTBnN9F5StdYpUqnze5YkimwT4fawO2O1m\n5162gf57mNasyOiVv/Fa4XatDDYJz8Qo/HNThWZmQpid8sXTcPZJ6dr9931I/snf4K//NAKAYjNW\nny19NiUzacuSlTtVodi4u1e3ORKnf4PganGXKELUkjnUTrICKf6bH0fJyaNF421RGehrqn1vKC28\ni6fwWQAAIABJREFU5o1o/TaQmeX+89Vgczkx+/YrsPs8IIIAIgiQXE4QvaVAA6iiIrK5g9UPPmHy\naj1Au/3nQ9sRPP10A9SsKY4Ag5M+TF9uv7FIE/hzAN8lhDwF8J3c3yCEvEwI+avcNhcAfEUIuQPg\nQ7Ca4N4OgtOZggSXUXaWNy/Hk4XtVE1yot5606zMalazckEe7aTP9WpOHSOdKbwm8SR7TXj9tiAU\naqo9bnY/fz/4/5lMcTlaJV3hHoIQgomrlzD/7usYuXgGo5fOYPG7b2H47FKnh9ZUtsJL+UD49Lup\nugLhRCQFQdQ/92fTckXd90r0XzlELXAt4FqXzI0mWT3SnXcf6xuqzejmdSRzwbBZFki3gSN3kmzj\nicw16MfSd99GNrdaIIgilv/hw5qPo2Zl7N15CPfIEES7Dd6pcYhdXGPWTv/5tdu7FVUhzr05h8Hp\n3pD2KYVSGgTwbZ3bvwLwP+V+/wTAlTYPrfUkU4VMo9OpX19KwIKy42iuXA65ZuoGH1tRmIKBRQGe\n+dUmIbRmUqVIIhCNFayvFaW2VboeLT1x+n19k/k1Yiu8hOk/+Rv87Mffw4/eHcXyR0cQiFR1CZzD\nbTOUthREAUKD0qBWJrgeas0WV7q/J/T/uoR0ub5i/u98Vl7I1Y7ZchmfGq4UeRdyG00ytNhcTthc\nToh2GwJLc3Vp/8Z29rF/9xF2btzF0//6K4RXN1ow0uZx/30fHH/7UX7ZrNSJsBlQSpGKmi95Sw6x\nZwNgCxQues2mXD5XyDLbVu/cSgibP/y+wtK95RzYOEYmRkUrp0pB5akUo2QSzxp3IdlEEnt3HmL1\ng0+w9cVtJEPHnR5SR9gKL7FekJkDnH43BZVWbyjj9DrgGXSVfXQEkWD81CBIg6WEJy/6Ekih49Ss\niY1T7RWm0XZG7kFcB3HAC/g8bLLtsbqmjsBfz9J6X73MPLcerlVbMmMwCbeZsSvnMHrxDERHA58L\nCuzevI+tL7/G/r3HSIWbH2A2g/vv++DZPy5yIGwmhBBIdvPvu5w5wUvY/QIPhPUulKspjSJg8zGv\nPSWEXUz7PC0Z7onCKLitVv5MVQv139qfrNyVLn2p4yie/+p3CK2sIxWOILq5i/XffI7j9e1OD61j\nyJ/ewk+u1/5enX1rDp5hNwSRQLQJIALB0Kwfsy/oCeXURveukzYb3imsdZWTFfMr/FoCYKPjGGUh\nve7yK2OXg3XKduEXuqNoJXFqKV8hRNN4USU8eHbYgGS62OGozRBCMHRmEUNnFkFVFTtf3UVkc6fy\njjpEN9jEG3q2hsDiHMZf6A7JtHYycXYYWw/2DZvibL1njmGhRyJZUBHgc306U13zG59njLSErdK2\n+hCE4ubB0jncYa/uvJfOsO20jn9dqh+8e+s+1JKSOqqo2Lt9H77pcQg19HycdGwOCZe/vYRkNI1M\nPAuX3wG7qzl14CcnE+xxF+qNeBMb9zXXg9eFJnQ6XUu3M0MvYBNFY5H1Sl3M+WPkGgr8PsDn7d8s\nsstROKFVk7nXkm/AqPFjzi2TPa7aH7NFEEHA1CsvYvTKuYIxSx1QRUX4+ToSwe5wHizlXyxl8/rB\nzWb6wiiG5/y69wkiwdT5kaY/pkWHSCSZVGY8zmqAq22QNdMS7rHmK1MkkT2fdgRiTgfLpPNzm955\nTxSrn9d4RjiV7toAWFVUpEJhw/tTJ7QsAkBeEq+eRmiXzwH/hLdpATBwUoJgUdDPIPK/daVZMqzD\n1SwI0louG29UXmPmchoHz9UEbKLI7D/5kp0osGBRz4e9l+HSN3pZmVrhus+17uPsrtd0+MwiZl6/\nVuwAl8tckSoDdqqoiKxttWiE9XPnR8t4JxPO14w1OxAmAsHpV2dx5Xun4PDaQQQCQSQgAsHYqSFM\nnO1JWTQLIyhl6jy1YFY6JYosY9lpHHZgIHcuGfDWFpwLudUxt5udL7xufbe2ZiGJBVnLSq6rNoPn\nIUntC9ibBHuaxs+VlJzns4kk9u8+wtpHn2H7q7tIHVeW1uxFeP9HuxSBquFkrP9VCixVtbANl8zJ\nyrlMYIkuodZIAyh8MUuXeCgtdB4PeIonALNmp2qauFwO/YDebmNqFL1p+VpOPaoGRjXCRrdVosHO\n00ZQsllENnaQiSXgDAzkl9C846Pwjo+CUorEwRHkdBquwQAUWcbaB59UdexKZhGdoh1GGqJdzOky\ns7XyRnUmLXoQUWTf7VIVmHTWeFWNr9R10qHM6SjWSiekkPyoplSDu6lq50JRZM1/iRbU41drbASU\nn7cEgQXo2v0VFYh1vzEGEQR4x0cQ2z0ov08U4QgMQFUUCKKIVDiC9d98zuZkSpEMhRHd2sHkyy9g\nYLrxmtduo1wR6Agz3qGOjedkBMFmJ3xVZRlfgQAghSBUEMoDYMA4K1maaVQpm1S4j7retnqBczVB\nsNkVsSgCap/UFLcrODELnDsEmxi/YF7pigIiiti78wCi3YZsPAkiCrB5PBhcmoV/fjpfXzb5jRex\n89XXFV87JSuDqmpZRqIbaKX/PKUUDz98jnQiy/T5c/IA+8+O4PTaMXHGygb3NQSAx1N8catSFljl\nS+ByNcW68olgc6zeMryYO2fkJRpbMH49syC+YlUpCOalYXr722wAWhAE1zKnyiXj97h0AnaBBf0t\nNp6isR0gm4HawAXP+NVLSH34KZtrFQVEEEABOAMDePp3vwKlKuweDyhQXDtM2Wrd7s178E2OdeUc\n3Sj33/dh+oO/wV//xQ86Hgj336urh6rqd6ZqO4bVkgBUFMwnMaNMo6oy4e9oDHnv+kqyMNrbJKly\n9tGwPtnkvl6kngbBSktuRvt0EZRSbH12C6os5201qaJAzcrIxpO5v1VkIlEmv/PPH0PJshOIf3YS\nZ//VdzHz+jVMXL8Ch9+ruyoX3zvA1pdft+051UqrjDSiBwlk00rZd1tVKLYfHTblMSy6GJerUBqn\nNTryaDRszeYdAuh2VrpdrETN6WAB6YCv4EDaLMzOCwTVyXGantPqGVQFuF57KXpuqpLE3gevmxmb\nGAXsLa7NprEd0Ewa2U9u4v77PmyF6zOwsLmcWPreNzH+0gUEFmYwfOE0HANeJA6CoKoKUCATiyNr\nlNmmtGvVfJqBnpFGK3pBKnEygmCABabcVUh7xW804SlqfZOCSguKAjYb+6k1MOWTp2AQ0Blq5dL+\ncirik2O1FsitePwOdIOnIzHI1WYgKEUmlsDzf/4Ya7/9Ak///gOsffQpVFmGf24Kc++8BkHUORlT\nitjWLo436lObaAc8EC7YbjYeCKcTxu9nNt0nKygWxpTa7gKFxizt7Rmd4I1LeZUu29ttxZJqfN72\nuJobWFYqc6s0NyqK8XhU2prMdSZbXmedNyOSWZAcS+RKMnLa7pIEODtTe80zwPKnt3D//cZNLARJ\nRGB+BhPXLsMzMohMNG5o/FA2FjCFoH6GB8Ja/eB2B8InJwgGWEB1HGVdw5GY+RW/qtbnWiOKTK1h\nwMvqrIwCWTMEIacfnDuOp6QuisvEaC0mKe2YwUNLyWRZVj2VZs/bSGuyEXgjpDbI5r83Yrtc73AU\nteaPjJxIIXlwBCWdQfo4ip2b93H44CkIIVBNZN52btw1vb/TNMt/nuMZNG5KdQ9Uqcxi0ZuYymGi\nWJ2AWylr51i+yleKXokCP6ZRs1cleDOYdincTAO52ot1rrNbun8rywu4fb2isJ9Umt0WS+TqkGmh\nbJBj9l41YGdfDcqNewh/UF7L2yipcLSmc5cgSnAE+t+8R89IYzN2lP9pdVB8MmqCS6n2gxhPskCW\nZ2arWW4CbdwBrnRCkES2RBTVLJskkrm6ZbHtFr9txW4H7BI7EWUygELqX2Y0et/TGTYxZ7KFE48s\nd0wT1BnwodEUElUUHD1dxeCpeQiSZBjoEsIc5gZmpxp6vFbCbTf/+i9+gJ/ctGP5o/rrx9x+J3yj\nHkT2izMygkgw1wThdYsuRq8Pg0NQ3jsSS+SUhUR2n5Ecl9F5oZoShVK49KX2uFm5YMucSBYatvlz\nycrVB7HpDDtfOB0AEQA1F5Q24/whCqwUhEuParWZzXSajS4UeO9MqYpTi+uBW4XN7QQRiL5OOQGI\nIOZrh0EIpl99se8zwVp4U3T8T/3g57+vwmh6X0gpJzMIrhZKWSBMCFvu4l24Zh/Mej+0/AuuquWN\nb1zvVhKLJytVBTLd2eXfMASA11ucSc+L35tldDSBrt528QR7DSUJecMU7WvfpMwvX1arByIIGL96\nEbs374E2oOJABAGp0DEGzywg+HDZYKCAkuneTDCnUf95LefemsPanV0crISgKhROnx3zVyfhn6jR\nWMWi90imypvezDKhilpZZi2bNVZBqHWVxeMuT6LYJJZt5kFkPMnmRUFgY6t1ZawVDmuiwGqitaoV\nTgc7lyU02XMevGsvKCqWcaiFi5RWawM32Axnhmd8BESSyi44iChi/MULoKqK5FEYdq8HgYUZSNV6\nBvQRd360jOnASv7vN98bbUmDtBYrCK4Gvtwkiq0pyucZimjc3J5TEAC0IeMrCGzSFUVAkXPZgyaW\nIJQqcZTe53QUsgNlS2RVmJOYTar8hNbCEgAeAGc/uZlbVqu9tsw/OwW7x43gk+fIxuIQbDYkazS4\noKAQ7DaMnD+Fo6eroDrPmYLCPdo5eZpa2AovAT/9pSYQdtYloSaIAhavTWHh6iQoRU4uzeJEkJXZ\nhbDTAQi5VbR0urGgMJUpn6946UItF7F6akT8mHZ7cSZVpSyL204kiZ0bVKU8c+zR0RrmySOewOH6\n+HyOjidZQJuVzU2iUqmWu6iWztn1NsOZQQQB8998BRuf3ICcTIMQAqqqGD67iMDCDABgcGmu6Y/b\na2hf+633gTfxYUsDYSsIrgUjpYdqMQrOFKVQz6vVLC6F39fKBjHeoQsgb8Rht7PxNXoFLgjs2HlN\nZgDJXHMizxzYdYLfWjHb125rqc6ndjJljRX1N1e4hgIYvXga6eMYUuFjpELHrKu4SgRRhGsogGwi\naVhHJzkccNRqLd1BtsJL2GqSljAhpNuEQSzagaw0t3+CJzCc9kIjdDpTezmV2cVYJy/UBFKc5QXY\nfBKLszlc0jQF6mG3FRtmAOx/rxuIRAv9N0bn1xaX+rUjAObYvR4sffdtpI+jULJZOAMDEOutGz8h\ntFpX+GQ1xjWKkZuQtplK7z7+oyjsS69t9Ioliidko8YFSpnLj89TaJrzuHKTS/OeYpk2I//dXaVz\nmo03dOgMyusuyN7wLmq3q+CAp6epXEoPRC2FALh+VFnG+m+/xOqHn2L31j0cPVuvKQAGgKlXWE1Z\nYj9o+Bnp5qY4M7i73P/yx9FcI0XnnYcs+gQumcYdPt3OyvMOpUAyzRquo/H6+gmMgj1uvNQptAYb\n+blbYHJzgL7ihhajDDeQU4SQWFZYrzGZqwO1iHYGwBxCCJyBAXhGh60AuEqKG6TRcIO0FisIroW0\nSb0ob6QqVWyQc00NvBuWgt3Pm7FKs6t6Rhil0jv8d5st58vurT84FAQ2EQ142YRvtp3ZkpUo5iyh\nXaw5wudlznYcm0m2oF4VDSPMMuUtXlZrFnt3HiIZDIEqKhNSr6Mjev/uY1BKIUiSYYOF0Gwt0zZy\n50fLLdEStjjBkFzWU5vdtNnYba1GVct1dfPNYO1XqQFQwWAjN3dUWpk0mtb5a+t2FRq/U7nSlEyW\nZZpb2JxcumrXjgDYon5apStsBcG1oOS0hUtlc3g2l0uwJZPs92icXeFmZfMghoCVHJRaYhZtozMJ\n8f8JKTTtGcHrfB32QjmCKLDMMq/bMsvCEsL2NXocbm8paIJ1u71QK2dU4sGzCs0MgI1u55qVrUSn\nGY6qKpR0psyeN75/iPXffoFn//hrbH5+K+8XryoqIhs7NWd+S8nE4kgcHsE7Oar7shBByNei9Sqt\nMtWwOKGULtsDxUmHVpNIsYCXN7zJMjuPtHreMqLStExgHKjyTK5cQWo0n1l2suRQPHcubaG1e3nZ\nmkUv0Apd4d5NA3WKrMwCXZ6x1auTNco2crcbrvKQyRYCU35/PUs/2qtyPXhwrf07nam9xpmPv7Rk\nw0zixmFn2Q2jSZxfSDQaCOcF2JVyiTlOi08kNLQGSmm+GY6qKvbvP0F4ZQOgKogoYeTCKQyemkf4\n+QbL1OY+P9lEEvHdA8y++TIcA968nW8pRCAsmK7iY0IVFalQBJ7RYUy/+hK2Pr+Vv52IIpyDAxg6\n2/vZj27zorfoYSQTh09JZHNZq8lk2E83YBaI5lzPQFUWtJYmSHhdtCCzOdlIng6ofA5rAQU94OYF\nwcmjMPbvPUYqHIFos2HozAIGT82fKKmzVqPtC+EN0tryiFpVg6wguF5qbRLjEjJAbkKVCuUF1YqE\nV8LjBpDLePJAXBT1s8uOOh15KGXPRVu/Vk1Dh1bkvXQsqTRbEtN7LE41r0s8mdO/NDiJ2aSWNMUZ\n1ZXt3n6AyMZ2XuaMqlkc3H8Cqqo4fPgsHwDnj6Oo2L39AIvffhOS3Q5ZR66tWrchgKkgcJkd78Qo\nTv3+u4hu7kLJZOAaGYR7ZKhvJudu8qK36GH4Cl/p94JfZHcDXLtY607aKiRNyUOZXq9mfuLnHN7Y\nrF39VHONg66chjDQE70dtZIIhrDxuy/z870sKzi4/wTp4xgmr18GAFBKc0kIoW/m3k7RLF1hKwhu\nF6XalLV+AcyuovmkxK+kJYkF6bJi7jdfD4SUy6VV29ARjbPXgU+EKmWlI7LCglO9wLya14k/f648\nYXQSE0VgwMdk31Lppiy3GQXASjqDyPp2WUkDVVgATIh+MjcTiUFOpTB65Rx2btxtTJqOEPimxvN/\nSg47Bk/1rwSPnqmGQIqnuHpUJCxOEOmMcUayQ+Y5ebhWfanGcSxRf4DudOQ0jsHm4ZSmDIGr+ejq\nrSfLA3CuiqGHqrIyB8C496SV+r+ltEAPmK3slc/3kY1tDJ9fQmR9G0dPV6EqCkS7DSMXTiOwOGsF\nww3QDF1hqya4HfCap0bRKk2UKlKUBtg8A2ymPWnW0KB3H1e4KJ1wFaXczthIKSOeYAoZkRiQyOl1\n+n3FAbBRxljveHw7bW2x2ZKbkMvCez0NXyCYdRZnYnHm/KO3n6qU1Qdr2fj4JnxT47DpZcergAgs\nAzz39isQJJ1Gyz6mtGZs6Z1Y/qcTvvQWPYaiFBQJ8n0fuXmrhSoFFfG4WQJBO8fxH6+ORm+1x3TY\nCz0ctty8yOctM038RuYV/vrqKUG0GBrbAQ2ttUQNIhU61n9MSrH+my8QfLzC1HgohZLOYP/uI4Se\nrTXt8U8qW+Gl/E89PSJWENytlAah2uBOlgsdtKpqHOTyH6PJW7u8VbqNdl+txFs8WX4cIOcBr7O/\nzcYyDUXPDfpd2NqfajBqFiylNDPMH8NZpeyb3iErSOtIbpdxYxsRIDmMlTay8QSCj1Yg19oRTggk\nlwszb1zHqT94F84T4Duvh9aLXvvD5dT6NRAmhPyAEHKfEKISQl422e73CSGPCSHLhJA/a+cYe4JM\nlvV9JJKFi/ZO2tLbJPNaZZ70qITdVpDY9LqNj8nL9Ix6RhpN6mSyOedOmZ1TMlm2StjCRjig9XJo\notEKAqWQkymDVcFl04SIRe3wQJjJqdGKcmpWOUSrsUk5u1+TcgYt2qtjvYmG1xNTsCWsapbLeTBr\nFDRWaliglMnVGE1SLqdx1oA3x5U2epjVJNfbIFgrDWZJzQwxbC4n3KNDSBwEi+p4iShgcGkeA3NT\nWP3nj3WPSylFbGevrGbYDCIIGDq7iOGziz0te9ZM7vyo2Cr6nZ+dBv649V70HeQegD8C8JdGGxBC\nRAD/J4DvAtgE8CUh5O8opQ/aM8Qeols0tI0smTk8qWBGqfKQ2XzP50VZ1g+US22P60FWANkgodIC\n2qEHHFiaw9HT5zVZ3VOFKQedRIvkVqLXI2KElQluJV4PW8Lik5jeElDp3xyzSY0v6/P/qwkYq8kI\nm2F0lctd3swyuHqNc0ZZiFrGVA1mr02LA+2pV16Ce2wERBCYVq8gYGB2CqOXzsDp98FuUBtHBAKh\nNHteAUop/PPTVgBsQr/rClNKH1JKH1fY7BUAy5TSFUppBsD/A+Bft350FvVTYZ4iMA9KeSJCbzVM\nD37Rnsnm1B9Kytwo7R71iipolyHGyPlT8IyNgNRYZidYhhktQasrzOZ8fawzZqtw2FnNaemVN6VM\nZkdWCnJeoshurxQY6tGOrCkhgMMBgLCGMo6gM7kaYbMVywvVOmSjZrdqs+t6+zbYGFFJXke0SZh9\n4zqyyRTkRBJ2rweiJgM+ev4Utr+6W5bxFUQRI+eXsPlpuKasQvj5BsYun6v5eZwkuJxaK73ou5xp\nABuavzcBvNqhsVhUQyZr3Nuh7YswOg+IYmHlsBJc/cflZDW6sRJVB1lhzcw9soLfTBv7ShBBwMzr\n15COxLDxyQ3ICfNMNxEElrhodvO6RR7eLP3me6OG21ivfqswW8LKyiwA400YyWR1AXAjmdxKx6gE\nzybwDKXDzlzhqt23tFwiK1c/ltImQP47l+ExbfADC9y12/F96wyCaWgNNFN9va7N5YRreLAoAAYA\n3/QERi6cBhFzmWJRhM3rxvw3X4VnbASBpbnqswqUIhOL1/I0Tix6NWOFn+7ODhNCfkUIuafz0/Rs\nLiHkh4SQrwghXx0c6jf9WLSBrFzeeMzhc7/ZcjqlxgFw6YokzxDbbczJk6s6HEfZTzzRmGJNG6Gx\nHdBMuu2GGI4BL4ZOzxs2RhNRBBEEeCfHMPbChbaN66TCG+aMaCgTTAj5AYCfALgA4BVK6VeNHO/k\noJmRuHe6ETyLWSlgLM12GtUA13qc0v2ddha8G+nxGkFQ6GJW1drcl/SyuKkUkNZkln0eA8tpFJoI\nJYllr+u0IQZQd2exqihIBsMAANfwYP7qf/jsIgaXZpEKRyDYbHAMePOSOeNXzsM/O4WNj7+CUilg\nJwSu4ROV0WwIbc0YUFga/skNCcsf0a7VGKaUfqfBQ2wBmNX8PZO7Te+xfg7g5wDw8tUzvRH59Ctc\nA11v5Y0rOxihKMVBLoevhhke0waQdP2Jkw7CA+DDn/6yI3bIgYVZHK9uIROP51fziCjCPTqEwdPz\ncPi8sFVyebVoC42WQ1RsxDixZLOAYJANlnPBm8POJq9KwaRKAVUplE2YqSBUs+xldIxqa4tr/fJy\njV6zRrxqZdHy4yi5yk6mmORP6SSfyRSO3UCjSyN1ZZHNXezevAv2xrA3aPLlF+CbGgMACJIE94h+\n0EVVFWoVnelEIAjM97YFcrvhS2UBzVLZz16/mnMhOqrZeahH+BLAGULIIljw+z8A+B87OySLqlAU\n4/m90twdS5RLqcmysT47RyCA0ltBMHfu7FQADACCJGL+W6/heG0LkY0dCKII/+IMfFPjli5wl9FQ\nEEwpfQjAelP1SKdZgMt1HfkklUoXlpMqdv3mgkUCgJg47fDl/USS1e46Kxy3UZvialUVjBr9qtUt\nNlLIAMqb7WSFLdU5HYWLhVRz7EcbCYDTkRh2bnxdVtu7/eVtLH77Tdi5i6DJ/oZSaxpGL5+HaKbr\naaHLVngJW+8X/p7+4Jf42Y+/l7fj3I5HeiYQJoT8WwD/GcAogL8nhNymlP4eIWQKwF9RSr9PKZUJ\nIf8zgP8OQATwXyil9zs4bItqycqAniRwNf0Nqsq02flqmKJUJ0nWI6UPnFZpANeDIIoYXJrD4FL/\nGhT1A1ZjXKugYNqHdhtbVuJZSW1Wz6xOC6gcOGpJ5orw7VVklo0CYEJyk16FkohakJXqPeEpgGgs\n9ztlJQ5m2+o9VixR2/gqDanBzuLQyppucxtVKUIrGxh/4bzp/pLbWVWGfv/rB4jt7GHipYsVA2sL\nY7S+9L/+4wD+03/x9UwgTCn9BYBf6Ny+DeD7mr//AcA/tHFoFs0inmArXlq0GV2Ho7DglE6XB8d6\nq2GpdHl5G6U5dYjeCILbpQBBKUUyGELiMATRbsPAzKSVfOhxKkYnhJBfAZjQuevHlNK/rfaBCCE/\nBPBDAJibMe7U6zsyWWO7TVkBbDWYQ+jBs8AUbLmrkoh5pcxzJmOu+VsrXMKtmqY/bekCYN6B3IbJ\nuRkTa9bIXIRSZBOVA3ZS7dOkQGI/iNUPP8Op771d1oRnURt3frRcpisskJPlvmfRhcgKa1DjspSy\nwrK6DntxIEtQaJarlCXm92ub6zIZoFazng7RrgBYVVRsfvwVkqFjUEUBEUXs332MmdeuwjM+0pLH\ntGg9FYPgJjRi8ONYTRalpFKAzVscJFLKJjajkgO9gDKdLpQB1BtQ81KEdC4QrVa8u1ItbzVlFyoF\nqMqeh5Zstlhmrui+1grZ88YK+dNbDUnruEYGkTg4KitpIKIA52AAwafPEd3YAQSCwMIs/HNTIIIA\nSim2v7iN6M5BbeNWFIRWNzBy7lRd47UocOdHy3jzj6LAH38L/3XFyvY0Cg9WWo7NDuKdbP3jdJLS\nxIpeozIh7PZqVHDSGfYjkJ4qgWhXAAwAR09WkDwK5+dyLm25+fktnPn+tyyN9h7Fetc6iUrZ8r/T\nkXOBywWhmSygZ6SgVzObTLN9q9Xr1YMH3skkC1r1JlQzeTY+rtLlNJXqG2WUHldv2Q5gt9ltxYE0\nz3w36lhkNqSyALh+BhdmEXq6CiWjCYIJqxeLrG8jm0jkyyX2jmOIbO5g9s2XEX6+gdjuYc0qFlRV\nkTwMA5ZccFPgusLvvH41f9v/0cHx9Cra7xTT124dIz/+HhBaAxmcb+njdA2VVv8Eofp5pEcD4FZr\nAANMh92oPyO2e4CBmT6/8OpTGpVI023EaMrITgoqBRKp8tvjScDjKr6NN38JQqGemNd/NtqcGM8t\nzbtNltGNyhr0mtrYDuUqDnqYZSqi8ZyKhqauuoVZ4GZ3FmfiCUgeF5RsNl/e4RkfhWvIj+CeavmP\nAAAgAElEQVTjlaJ6YZqTUYvvHSK0sl6TbXIeQmD36XXPWNTL/fd9wPvLlTc84dDYjvF9RXJVLdZs\n/ekvEXhvFLY3ANiM57O+yRZXCnBpfXKQ3Ubp56tZiYpqUY3mY0qrUvCx6E4aVYfQbcSwaAKyDESi\nOa1GTe2X3caCQn71X02tbaU6YK0LnJGdJsl1FJuVN5TeLgiVJd0UtbBkZ5Rt5kt1LabZAXAiGMLG\n774sbowTBNhcTsT3g/oNc4qCyNYu1Drl3IhArG5ki7ZDQ2sAAOXGvbL71HSmrd36W+El4IMVBHAT\ngkFtvHj9Mmhsp38C4Uy2UCfM4e6kvZPcNYRnffnnq92fKQDwjA0jurWnMzjAPdqduuIWlbHKIboZ\niuLaLyOxdMP9NU5r2nICjqoyGTGtnXE2q+9eR3MClbWoRtD8P8b7SSKr+3XYmbJDC8scjGhVXdn+\n14/KA11VxfHaFpxDfsP9Yjv7cA0NQtZbITBAyL1nUy+/YKlDWLSN0u+OEe2Wq+KBsBGBdAa2N671\nTyCcTBVMM7iWcFbWX2XsMbTJCS3t/kyNXjqL+F4QqiIXTmuiCP/8NOylih0WPUPPBsFmS2+tpGMT\nJrctrsmljRTqfXkJRSZrnnXNZJnMjoByiTYB1Sk95PcBc3UzUirIdzLn/ve4mJZlG2llY0UqHNG/\ngxDT7IyaySK+u1/149gHfJh46SJcQ35Dq04Li4ZQMvpzbpuakurBdDy5TDEPhI3oqQA5kczN00LB\nJr7H6SbdX7vXg4Vvv4Hg4xUk9oMQ7TYMnl7AwGzhM5IIhhB8vIJMNA5nYADD55bgDHS/vOJJpieD\nYG3g0k46mjkQK5QWGEEIy7YeR6vfJ5Eo1BqXHkuvCc6MjEkQrHf8Wpo4GqTZAXDy6BiRzR1AVeGb\nngARBVCdWjFW+xtq6LE4RBQxdvks3COWZbJF66CxOLK//kz3vm4IUGqFm6RcgvE5RHr9KtBrmWJK\nO7Ka1mzaqfpQC3aPG5PXLuved7yxjd2b9/Krf9l4ArHdfcy8fh2eseF2DtOiBnouCC7vCG0P04Hi\nzEHbJ8ZKtbU8QG2Ge1+9xyotu0ilWUCblauzh24jzZ5k975+yLqHcxNgeG0TgihCQfNOSEQUIIgi\nVEUFIQSUqhi9dAbeiROku23REZIh0WS+bd883GzMziHTH/ySKU30WiDc43RrAGwGVVXs3X5QVv5G\nFRW7t+5h6XvftJx1u5SuDILN9CQppW3tCOVomy1sb1zLN4KU0SqNSkXVtzvm5Q6gTCpNd98aAzFF\nNQ5YeVBrVJqhKEzxIp0puBMlkoDLWW7CoVd33KIssN5nqlnSOolgCOHnmyVKDyqU0nrgRjU4CcHE\ntcuwuV1QZRnOwIClTWlh0SK2wkvAT3+Zl1wz4sRIsbWBXgyAgZy9vUH5iZxMQ0lnIFWrvW/RVrru\nDGpUBK/F6IuhyjKCT57jeG0LlFL4psYxcuE0pCa5Z/EltOkP/rvu/YH3Rlu7hBZPMlc43vgAsECT\nu5KVNs7xL2Wtzj955zh7ecCdTLPHtEmFgJw/TiJV3GSnJZkqNG8QwiyRtRlsgAXLLcBIo7RZE2xk\nfauynBkhcI8MI3UUqltOhxACz/goBNGq+7WwaAc8EDZCK8VmZYsbo926v81EkETDGmxKKYg1Z3ct\nHQ+Ci5oSspm6JaqoqmLt118gE43lBa3DzzcQ297D4nfeaqq/t9HYWIBcWEIzou7JUlVZ45gksgBU\nVoozp7z8wGFn+ryKwlzpdKS4KpJMs6wlD6pVtRAAA0y/12ZjY+E+89VkcXlNcSTGMsOSyMaXybRE\nqL1c9qz5E6uqVDFuSpGNxevqVSGSCEIIZt982QqALSzazIlrsGsTndb9bSZ2rwc2twuZaLzsPvfI\nIESb5TjZrXQsCC7V/QMa0/6L7uwjE4sXO7pQCiWTRfj5OobbZCPLMweB90ZNNSobWkKTFcCo1jST\nLbfUrJdK+rzZrHHmtxLcHa+F9vTN1v01YmBmAtGt3YrZYKqqmLh6Ebu37utqBJchCBiYHod3chze\nyVEIooGVtoWFRUc4kZrETaAbdH+1qLKM4/VtxPcPITmdCCzOwqnn2mrC9KtXsf6bz6EqKqiigEgi\nRJsNk9dfaNGoLZpBZ4JgJWOoLWn0JZBTaYSerSF+EITN5cTg6QW4hwsd8fG9Q90ghKoqYjsHbQuC\ngWo1KmEtobWIdteVecZH4B4ZROIwZBwIEwLv1Dj8c9Owez04erqKZOgYcjKlv4xGCNzDAUxev2LJ\nnllYdDEnTpO4QbpF95ejpDNY/fBTyOl0PjlxvLaJ8RcvIrAwU/VxHANenPr9dxHd2kUmnoBjwAvf\n1Lg1f3c5nQmCKa0pQMnEE1j98FNQWQFVVaRwjNjuAUYvncXQ6QUAYOUO2vpSDWKTaoJrwVpC6wyd\naKwghGDm9WuIbO7ieG0T6WgcikaLmQgEgs2GkfOnoCoK1KyMwOIMJq5fQfDRMo6erpZ/bgkwMD9t\nTaAWFj2A2TzDpdh4v4iWfpzjTTX8u7Dp7eDBU2RLkhFUYWoPvqnxmkopBYmZZ1j0Dh0Jgmk8UdOX\nYP/uY6glS/xUUXFw7wn8c9MQ7TYE5mcQWl4r69AkoojBU91lI2stobWGTnYWE0HAwOwkfFNjgCAg\nvrOP0LN1KNksPBOjGDq9gMRBEDs37uWkctjndPylS/oHVCkO7z2Bf3bKktaxsOhx7r/vw/QHxWVy\n4vXLoKG1vlKX0Ctz1FJv2YOSySK0so7Yzj5Em4TA0jy8k6NNmRsjm7v6q3ECQXzvAAOzUw0/hkX3\n0pEgOBkSa/oSxPf07TiJQBA/CGJgegJ2nwfjL13E3u0HOUUDClBg6PQ8PGMjTRp587CW0JpLp6V1\njjd2cHDvMeRUGkQg8C/MYEbTyJaORLFz4y6oohaZxe3evAciCLplFHIqjeX/9hECC7MYPrdo1QRb\nWPQwZXP+f9vKy6/1QyDcKgttOZ3B6gefQEln8j0/iWAYgYVpjL94saExm0FlBbu37iMTT2L43JKV\njOhTOq4OUQ2EEEOXWe1ycWBhBt7JMcR29kFVFd6JUdjcrvYMsg6sJbT6KVty66C0TnRrD7s37+br\nyahCcfx8E3IihZnXryETi2Prizu6zXC0gjaykkrj6MkKEodBzL39ijURW1j0MGVzfq6JmveIaOnG\neb4TpQ7BxyuQ0+ki9SCqKAg/30RgaQ4On7eu49KcUpFvehzHa1u62WBVVhB8/AyZaBxT37Aa3PqR\nngiCfdMTOF7fLvuQUkqROAgicRjCwPQ4nIN+SA47AgsziO0dYPOzW8hE45BcDgyfOwX/XG8tLWuX\n0DjS61f7JnNQL1rdXy2dktbZv/+43ClIVRHfO0RoZQP7dx8aq0FQCsEmQZVlGF3pUVVFKhRB4vAI\nnlHLftPCol/QlsZp6cZVQKN5V0srkhDR7V1d+UwKIL57WHMQLKcz2Lv9ANHtPYBSOAa8EO02qLKs\nn6hQVES3dpG9dKark2oW9dETQfDY5XNIHIYgp9Js2VggrNqBUoSWmZNPeGUdAzMTmLh2GdGtPezc\n+Lrg4R1LYO/2A8jJFEbOt08lohlwgw5OXof4hAbC7ZI9qwZVVkAVBdlYQn8DQrB3+77pMYgoYnBp\nDpHNXdacYZAVpoqC5GHICoItLPqMsjk+UNw43Q2BMA+AOzHvEqLfHEyAmhuHmZ/AZ8jGk/mkWjoS\nAwQBQ6fncfTkuf5jCQSpUMQKgvuQngiCRYcdi995C9GtXSQOj0ApRWRju2x5JLK5C+/UuG7mjSps\nWWPo9HxPW81qdYhtb5hs2MPya4aW1EBXBMBKOoONT24gFTo23a6iixwAUIrg0+csrVHqoKeBiAJE\ne/tVTiwsLNqLNjtse+Oa6XzYLjo57wYWZnD4cLnYAyCHb2qspmPFdg+gpNLlcyylkFNpiHYbFB2d\nfVVWoHCjKIu+omeiQUEU4J+bgn9uCjs37+kvjygKwisbTJ5KB0IEpCMxuIYCrR5uS8k3WJjYN3dT\nFqFaWtVY0UwopXj2T78tUyup+3ilE7uRpRwFfDMTTXlMi/6EEPIDAD8BcAHAK5TSrwy2WwUQBXPc\nkSmlL7drjBbVwbPD0wZzfCfo1Lw7eHoesd19pMJRllggBIQQjL94AZLLWdOxUqFjfdt6SpE8CiNw\nah7BR8905+H9e497rqTSojI9EwRrMXPbolQFWyjRCZKp2jfZtHp1iNsVFJs2UBjRhRqSpUS3900D\nYEGSoCqKcTBbJ66RQUgd0Lu26CnuAfgjAH9ZxbbfopQetng8Fg3SrfNgu1BlGbHdAwzMTcM/B6SP\noxDsNvjnpmD3emo+ns3tAhEF3RjC7nZh+Owigg+X9XdWFCSDYbhHBvXvt+hJejII9k2PI7q9V7bc\nTEQR/tkpSA47s7HVZosJgcPnhd3rbvNo2w/PIlwq0SFul/ZwJa1IIzptnVkN8d190/vVFi2ZyclU\nS45r0T9QSh8CsDJVFn1BfO8Qm5/dylWIMclT//w0Ri6crvsz7puZxP7dx6AoDoKJKGLo7FKFGmPS\nsvndonP0ZBDsnRyDa8iP5FE4f0VHRAGOAQ98M5PwTo0jG08idRxlOxBAcjow/dq1Do66/dx/34fp\nQEGXMpDO5CXXWhUIazuIzUoa9GDBb2cUHqqlU40RthqX/SwsTKAAfkkIoQD+klL6804PyMJCi5LJ\nYvOzW6CKUrSme7y+DdfwIPxz9RlYiDYJs29/A1uf3swFtASUqhi7cg6eMdZ07Bz06/Z7UFWFa8hf\n1+NadC89GQQTQjD75ss4Xt/G8domqEoxMDeFwMIMMycQBcy/+xpSoWOkIlHY3W64RgZPZIZEm1Vl\nNWY5dYl6yhWqoLiDuLsD2noYOrOAQ6Plshqwez1wjQzieHWz4rZEFDB0tnuz4xbtgxDyKwB6xeE/\nppT+bZWHeYtSukUIGQPwT4SQR5TS3+g81g8B/BAAJlz991226F6iW7u6t1NFwc6Nu4hu7WLk4hk4\n/bV/Ll2Dfpz6g3fz9cGuIX9Rs/z4ixew/tsvi1aaiShi+Oxi35RTWhToySAYYNIogYUZBBZmim6n\nlIIqCogowjnoh3PQunLTolWXaAXdXs7QKEomC8/kGOI75mURRgSWZjFy4Qwkhx2UUiiZLGLbe2Xb\nEVFgJjGUYvTS2XyWwuJkQyn9ThOOsZX7f58Q8gsArwAoC4JzGeKfA8DFwfHmFrm3EEopk72iFA6/\n70QmP3odJZMFVQ3UdShFbGcf8f0g5r75Clx1nOMJIYYN8q6hAObffQ2HD5eROgpDcjkxfHYRvukJ\nUFVFdGcfycMjiE4n/HNT1ipdj9OzQXAplFKEn2/g8MEylGwWgihi8PR8Q/VD/UqpLmVz6d+MUeLw\nCBsf38g1X9aOa3gQEy9dyv9NCMHMa1cRPzxC8PEKlFQaruEABk8vQEllQBUFzqEARFvffE0tOgwh\nxANAoJRGc79/D8D/3uFhNY3E4RG2v7gDJctqNwVJxNTLL8AzPtLhkVnUgmtkEEQQTWUmqaJg/+tH\nmH/n1aY/vtPvw8xrV4tuUzJZpjGcSLFEm0AQfLSM6VevwjvRmqSSRevpm7NraGUdB/cKzl2qLOPo\n6XOomSzGX2qdv7hFf3O8sY3QszVkk2moGu/6enAbGF14RobgGRkqvrGOzmeLkw0h5N8C+M8ARgH8\nPSHkNqX09wghUwD+ilL6fQDjAH6RSwxIAP5vSuk/dmzQDUJVNS+ZlU0k2UWqJnBSFAWbn93E4rff\nrEtNwKIzuIYCcA0FkAyGTOfc5FG4bWM6uP8EmVgir/zDGu8ptr64jTN/+B4EUWzbWCyaR18EwZRS\nHD5Y1jHIUBFe3cTIxTMQ7bYOjc6iF8kmU1j79eeQE8mmHfN4bROpUBhyKg3HgBdDZxbhDAw07fgW\nJxtK6S8A/ELn9m0A38/9vgLgxTYPremE17Zw+OAJ5GQagk3C0OkFqIrCVARKoCpF6Nk6xl+80IGR\nWtQD6/u5juCT5zhaXjWUpWxn4BnZ3NE3MgIQ3wvWbNxh0R30RRCsZLKGyyZEEJCJxcvqf+IHQRw9\nXUU2kYR7eBBDZxdh99Qnn5Y+jiK8tgklnYV3chS+qfGa7RwtugdKKdZ/09wAGGAyZ1zqLH0cRWRz\nF4IkQs3KkFxOjFw8jcD8TIWjWFicbMJrm9i7/aCw6peVEXyyAtHh0LcdpxTpaKzNo7RoFCIIGDl/\nCsPnlrD89x9CyWTK7vcvTLdtPGYZ6UZWCC06S18EwaJNYpdjOlBVheRyQk6mkEkkYfe4Ed3axb6m\ndCITjSOysY25d16ruds09Gyt6FjR7T0EH69g/p1Xe9qe+SRzvL7NvOVbDaVQc7WLcjKFvdsPoMoK\nhk7Nt/6xLSx6EEopDu490V31k5MpfdtxQairecqiO+BZ4fXffcka31UVhAhwBgYweuls28bhGR/V\nbWKmKoVndEhnD4teoC+iNK4UEX6+WXxFJghwjQxi785DxHcPQAQBVFV1rWpVWcHah59g5MJpDJ1Z\nrCqTm02mmPC25nhUUZCJxnH0dBUjF0436ylatAlVlrF3+35HHpsqKg4fPMXg4qy1kmBhoYMqy1AM\nl8YFUIqyVUFBEDC4NNeO4Vm0COegH6e//y3Etvchp9NwDvrhGgrU3fROVRVHy2sIr26AKiq8U+MY\nObcEyekw3GfsyjkkDoLMETRnxEVEESMXTkO03Dx7lr4IggFg7Mp5KJksolt7+WDXNTwIURIR2z3Q\nD35LoCrF4cNnSAbDmHnjesXHjBnIZFGV1SI7AwOIHwQh2u1MSqVDRgsW1XO8vm1qy61Fcjsx9/Yr\nEGw2rP/6c2SasORKVYpsMlV3aY6FRT8jiCKb33XK36hKMf36NSZtlTM7cPp9mLh+BZIlY9XzCKKI\ngdnGTZ4opdj43VdIhgpmW+GVdUQ3d7D4nbcM7entHjeWvvMWjpbXEN8/hORyYuj0giVf2eP0TRBM\nBAFT33gR8uUU0rE4bG4XRJsNy//wYU31OlRVET84QnRrD6nwMeR0BtlkCslgGIJA4JuZwOiFM+zK\nT6dIniOn0tj68g6orAACQfDRM0xcvwz/bH1ONxbtIR2OVL2tkmI1apLdhvl3XsHW57eRODhqbACU\nWoLsFhYGEEFAYHEW4ZX14nmdEDgH/fCOj8A7PgIlmwUorIZoizISB0EkQ8fFyQ5KoWSzOFpexZhJ\niYXkcmLsyjkA51o/UIu20DdBMEdyOfNX/elIFEQgqFXWlaoqtr64nfujEOgqAMKrm4jvHmLxO2/B\nMzEK3H1sdBQWAAOASkFBsXvjHrzjoz05MauyjNCzNRxv7IAAGJifxuDSXNtlYbKJJI6WV5E8Oobd\n68HQmYW6XIM4qqIgsrmD+M4BRIcdRJJyn5nK3gBEFJCNszpz0W7H3Nuv4OD+EwQfr1TcV/d4ggDv\n1JilC2xhYcLY5bNQUilEt/dZVpiqcPoHinRdRVvvzbEnGTmZgqqqsLldhiUOcioNOZ2B3eNqqN8m\nvh/Ub6RXmQmHWRBs0X/09dnW5nZVFcyUYZLhhUohpzOIbGwjsDiLoTMLOFpey3+piCiwK0y9QxCC\n2M4+BuamkI7EoGQycAb8XR/0qIqCtY8+RyYWz2dfDu8/RXRzB/PvvFaxflVOpbF/7wli27ugFPBN\njWH08rmanXZS4QjWf/M5VEUFKEUqFEZ0aweTL7+AgWk9J1nz56QqCtY/+hzZJBM/BwHTHAWB/htY\ncgxZgd1XrD0areQkRwjsHje8M+NI7B0iHYmBEHYidw0NYvLa5Zqeh4XFSYMIAqZeeQnZRAqZaAyS\n2wmHz9vpYfUcXE6uk2ZSmVgc21/cYQ5/hF28TFy7XGQ+oWSz2Pnya8T3gyxBQSmGTi9g5OKZusYu\n2CRAEHSVRHoxQWXRGN0dfTWIIEnwz08j/HyjqcelioLY3iECi7MYvXQW7rFhhJ+tQ85k4ZsaQ/DR\nM4PmDYpsIonn//Q7ZJMpZourqhg+t4Th86e61tkusrGDTDxR3ACoqkhH4ohu72FgxrhOS5VlrH74\nKeRUOn9xEdncQfzgCEvffaumjM3uzXtQZc0VPGXNZDs37sLh88AxUDkjHN87xN6dh8jE4uWd5BQA\npaAE+bpycyjkZCofzKePo8jGE4Zbj145h+Ezi4UbLp5FOhJFJpaA3eexTuQWFjVgczthc5tfSFNV\nRfDJc4Sfb0CVFXjGhjF66cyJNs5QMhns3XmE6NYuqKrCORTAxIsX4GyzgoYqs+SKVvpMVtLY+vwW\n5t95La+hvvXZLSSCIbaimpuSj5bXINptGNLOp1Xin51C8OGzsjQHEUUMLlnKPCeNvm9BH3vxQks6\n7bVdpJ7RYUy/dhXz33wFQ6cX4J0cZwFWKZQ522VicVBFgSrL+Uk6urnT9DE2i+j2rn4jiqIgulUu\nGaPleH2bXRCUBJtqNovw6mbVY1BlGanjqO59VFbw/INPWLCd0+HVI3EYwuZnN1kADBhm/IlAQKu5\nIKHA3teP8n+mwhEYavWBBcmlYv6OAR98U+NWAGxh0WQopdj45AaCj5+x5fZsFtGtXax+8CkyJher\n/QylFGu//hyRrZ38RX7qKIy133zRdi3l6NYuU1ooHaOi5kvKMrE4ksFwXo2hsI2C4OMVXXOUStjc\nLkxcuwQiCCCiAAgC0xyem4Jvery+J2PRs/R9ECwIAiauXQKE5mVZiShgcHE2/7eqKEgGQ/kgZ+Ti\naYh2G4jmMYkowjM5ahhMHtZZR9pKKKVIHIagpPUliQBATmcQPwgaTkbx/UOD56wisR+sYTQV3j+V\nIhU+xvrHXxmO5eBBub6oEZKjugw170IHWD262TCjW3uI7x1WdVwLC4vGSB2FkQyGy77zqiwj+OhZ\nh0bVWWK7B8gmU+VBparW3ctQL+lozNDkKh1hCY9MPGGYxCpLrtSAf24ap/7gXYxdOY+xy2ex8O03\nMHH1Uteuxlq0jr4uh+D456Yh2Gw4uPcYmWi8/gOJAggFxl+4AEeuGSu0so79u49Y7TGlACEILM5i\n4b03EF5ZR2z3EJLDhsFT88gmU4jvHOge2iyD2QmyiRTWf/cF5FTatK46FQpj69ObECQJs29/oyyj\nKblc0C2xJahJtkiQRLhHBs3VFyiQjSeRCkXgGipf2ksbZJLLDkMpbC4n5ETl90SQCo2B7tEhiDYb\nZFl/YqeKgvDqZlG9m4WFRWtIBEOGJU3xmi7A+4dU6LjQsK2FUpZxbSN2nwdEFHUDYV7aZne7ocqy\n7v6iw97QKq/ksFv60RYnIwgGAN/kGHyTY9j4+CskDo5K5HVg2gflm52Ce3gQgiTCOzGSl7CK7x9i\n/+tHxceiFOGVdaSjMcy99Y0iR5vkUVi/TALIB9XdwuanN5lrWoUrbaqooGD1XRu//RKn/uDdoqvp\nwcVZHOcEybWQOgTsJ65dxtpHn0GVFWObbAKkYzEcr28hsr4FVVHhGgpg/IXzkJwOZLL6E6p2XJRS\n9l5VgMs1FR6bYO7tV/D8g4/1TzQAqKp/u4WFRXMRbTZDTeGT2gBlczkNA89K9dXNZmB6Egd3n0Ap\nGQsRBQyfWwIAxPaNV86GTi+0cngWJ4S+L4coZfrVl+CZGAERBAiSxIKx0wtwj+jbHgo2CVPXL2Nw\naRb+uakiDdfgoxXDTEPy4AjJYKjoNuegHw6/t6hMAmBf+tGLrZFliR8EsfnpDax+8An27z1mDWoV\nSEdjyMRi+gEwISAGsmiqLCNxWJypdQx42TKTKIBIIgSJid2Pv3gx3/hQLXaPG0vf+ybGrpwzlMhR\nFRWhJ6s4Xt1kTXSUIhkMYe03X8A/N81qwEoQbBLcY8MYmJsu6D+XPHUiiRDtNjZ+kdWSuUYGMXLh\nTPEYvW5MXr/Cuo9LIKKIgRlLJ9rCoh34DBRjiCie2ADKNzOpm4chooDhs0ttHYsgiZh/91U4AwP5\n+lzJ6cD0q1fz5wazpna9udzColZOTCaYI0gSZl67BjmdYZ39HjdEm4RMNI7Vjz5lmU1VZcGeQDB2\n5Xy+gcs7MVZ0tZxJmDdXhJ6tFwXXzAP9G9j/+iEiGzugVIXN48bEixfhHhls+nPdvnEXkbWt/N+p\n4yjCq5tY+Nbrpo5kSjrDZLtgUD9rILxMUTCQyN9GKfxz0/BOjiOxfwgKwDM2XLeOp2iTMLg0B8np\nwPaXd4oyzEQQ4PB7kY7Eyy5OqKIgGTrG0JklHD1ZyWd8JZcDs29ch93rgZxK49k//trgOQOzb76M\nbDINOZWCayhgGMT7psbhHg4geXRcJJ3n8HsxMFOblJuFhUV9iHYbZl67is3Pb7HFvtzFrX92EgNz\nJ/NiVLRJmH3rG9j89GZ+bqKUYvTSWXjGR9o+HrvXg4X33mCNi4oKm6dYJ9hoRQ05ZSULi0Y5cUEw\nR3LYi+wR7T4Plr73TYSfryMZDMPmcUOQROzdfpAvl9j/+hGGL5zCyLlTAABnwI+YSd1oKnxcdpto\nkzB5/Qomrl0GVdWWmU0cPnpWFAADACiFmsni4N5jTL96FZlYAnIqBceAr2h50OH3GU4wkssByeEo\nagjLo1I4h/yglCK0vIbgkxUo6QwklxOjl87APzfdlOcmp9LMueeFCwg+XIaczoAQAv/8NCSXE6nQ\nU939ksEQZl67iqHT80iFI0gfRxBe38bKrz7OZX/NSj8oBEmCb6qyjBC72HkZx+vbOF7bAijFwNwU\n/PMzLVEqsbCw0MczPoIz3/8WYrsHULMy3KPDsHtPtiW5ayiA09//FpJHYVBZgXMo0LBWPaUUse09\nHK9vgVImQ+abHq96vjPqD/FOjSH0bL1sbiYCgWes/UG7Rf9xYoNgPSSHHSPnTwNgcldrv/6sLBgM\nPnoGz+hwkUSaEYJJtpOYlBU0Cut+Xja8P7pzgNWPPkM6HMnr4QaWZuGfn0Hy8AiCJJvuRxwAABkN\nSURBVCGwNIfw842i2jEiChi7ch6Sw4GNj78szsKKAnxT47B73Di4/6TIQEROprB76z5UWWmoEUGV\nFezc+BqxnYP8uAfmpjB66Uy+/s9s+Yy/l6LdhnQkioMHT6tXi3A6Yavh5EkEAYGFGQQWZqrex8LC\novkIkmSqZd5vUEoReraGoyfPmcOa143RS+fgmxrLb0MIgXu4OauPlFJsfX4L8b2CE1vi4Ajh1U3M\nvnm9oQv/4bNLiGzsMBvsXIM2EUX4psZrLqezsNDDCoINYAFgeYBEFRXBJytIHBwVGzeUQtgVd/Io\nDOegv6L0ClVVxHb2ET84guSwwz8/DZvbVdfYU+FIrgHPILOpqiyTS2k+MAw9W0fo2RoIEfL7+udn\nEN89gJxKw+7zYPTS2byyweybL2P/7mOkjyMQbDYMnp7H8NklKFkZR09XdcoRVBw+eArX8CCysQTs\nXne+GVCVFYSeryO2sw/J6cTgqTndCXrnxl3Edg5AVTV//MjGNgRJxPgLFwAAgknDi5prilMVBQf3\nqwuAiSiAEAHTr75kyedYWFh0HVRVmZkEpXAND+LgwVOEV9bz81smGsf2l7cxef1KSy4G4nuHRQEw\nkCs/OwojurWHgdn/v707/20ryw48/j3vcRMXLaT2xbItW3Z5qfLWZbsqqa7qanSqO50VGSAZYIBM\nAgQBJsAMMEAmjf4HEgQIMEACDBozQX5pTGaAmSATTAaV6qS7C91ddndV1+YqeZUtS7L2naIoLu/O\nD4+iRZGUaGshVTyfn0zxibzvybo6vO/cc57/PT0BP8fefJX5e4+IT0xjeT20DByhsa8+01nU3tMg\nuIzseqrsc4nZxXxAVZaB5cdPWH48juXx0HP9Eg1lOvJk0xke//Cm25UtmwVLmL3zgNjgcTKJJNlM\nmkBzE41HuvFVEBhX1Fd9663/3GOzKd93aWSME2+97m4W2yLYGuXoG9eLvp6Kr5bttpZNpXn0/Z9g\nWTbGOASaG+l46SwjP3yvICBdGZ8gdmqA6ImjWF4PIkJmPUV8YrpkcL34cJS2s4NYdm7jXZndzxs7\nQtaXVtxufeWvTl508BjRgaN1u5tcKVW74lMzPPnpx/n1DmNM2brs05/eIdLTuecf5pfHyjdTWhod\n31UQDG4g3H7+FO3nT+3qdZQqRYPgMkJdbcSnSjd6cFLlA+SC43L1DTeXD7O9HpxslsS022Ai2BZl\nduiB20VuI8DL3fbZXNA9/mSa2c/vEepqp+dLLxXUp93K3xTB4/eTTqwVP2mJGwBWmAawPD75TCkM\nnoB/+w0LjsFx3OuytrDEyA9+UlyH2LjnPndn2J0AX3wBj89TtrwcuAG21WATbI263ZBLHLNxO9D2\neQuC/bIEvIGABsBKqZqTTqwxfuPDiufyTHIdJ5Pddf7vVtvF1Hr3TNU6DYLLaOztYv7uQ9Kra3uy\nC9UYw8rYBLbfz5P3P0Zyu+2MY0Co+D1WJ2eY/Pktul9+qewxIkLvK5d4/O5PcbJOYSDvGExFa6Du\n6oHbLMPJ53Vl0xlmh+6x/PgJjuMQ6mil/dypfLUJb0OAQLSZtdltmloUjGW7ARgya0me3Pxw21rO\nIpLf5GjZNp2XzzPx/ieFfxwEGnIpFtlUetsGIPlvsWzdyKaU2nNOJks6sYanwV+2Uo6TzbK+tILl\n9ZRsq774aOyZ2gaLJVj7UFassa+75Gqw2DZNR3RPhKptGgSXYdk2/a9fZ3boHgv3R3b9eiabJbm8\nwtKjoXyDied7IcPKkymyqfS2K5T+xggDX3+dpZFxpj4ZKmqTWam5O8PM33tIU38vbWcHefzuTVIr\nT1et4+NTJKbnOPbVX8Cb2+Fr+/bhv1W5ANi2iQ4eKwhWG3s6SUzPFW6SMzDz2V2MY9xNg5X88TCG\ncJd2d1NK7Q1jDDO37rIwPOIuoTqGSE8nnZfOFlQKcjuR3nHv2hkHbzBI7/WL+MKh/DHp1bWK53Wx\nLLdO+j58qA+2RYn0dLIyPrmpJKRNqD1GeNNmPKVqkS5zbcP2emjq79029aBS4rHJJlPP9Mm97GtZ\nUlHTC8u2WR6deO4AGHA3z2Udlh6NMfqjn5GKJ4pWrZ1slvl7j9x/ZzKsTpbv8rOXLI9N7NRxYrmS\ndRuy6QxLj8eLjjdZh9mhexX9DMS26Lh4tqA5ilJK7cbs0AMWhkfchZBMFuM4rIxPMvH+p/lj4pMz\nTH96G5PN4mQymKxDaiXOyA9vFsy9DbHm0hWGSqQg+CIh2l88vS/nJCJ0XT5H7/VLNB3pofFINz1X\nL9Bz7aKmQ6iapyvBO3BzXHcZuIrgDfjdLmK7fS3c1YRKWlxmU2nW5hfKHyAQiDaTrKBnvHEct+pE\nqQDSMSRy7S2zqYybulAJy8L2erbdhLid3lcuM3/vEcNvv4svHCR2eoBga9TdnFem2cd2+XN2wE+4\nsy1XnaO37uuJqsNFRP4c+BUgBTwA/q0xpuiXW0TeAv4zYAP/1Rjzpwc60DplHIeF+w+L5qCNykCZ\n5DqegJ/Z2w9KzlNONkt8YjrfCa/pSDdztx+Q2bpvpcQcnYqvsr4cL7s5e7dEhFB7jFB7bF9eX6n9\noivBO/D4fYQ624puI4lllWyNC4AlT29biRDp6aD/y9cIt8d2XxvYsmgZ6K+oAoS7MW+7iFSeqXOb\niJTdBbFxfTwBH2KVP0fJpUp4GgJ0XjhD7OSxit9/M8vrYfTH7xOfmCadWGN1eo7RH7/P0uiT7Tfn\niZT9uYXbW+m6dI62s4MaAKvD6B3gnDHmReAu8K2tB4iIDfwV8HXgDPA7InLmQEdZp7LpTNkFFbGs\n/EbmkhuacT/Ap1bXWJtfJD41g3EM/W9cJ9zd4c5r4q74YhXP0SbrMH93ePfnkEq7NXuV+oLQleAK\ndF85z/jNj0jMzOfLf4U624idOs74zQ/JptII4GQdIj2ddF05j7VRJkwkf0so0tvJ7NB90mvJynJS\ntxDbJjZ4jNjpgZ0Pxg00bb+PbLnUCWNYnXqG1AW392jJp5KLyySXVgg0RWg7e5LpT26XDkSzhpPf\n/Eo+zWDx4ShiWxXvcN4YhzHFq7om6zD10RAnf/kNGmItJGbnC8YrloW/pbH0yrdlETt9vPIxKFVj\njDH/tOnhDeC3Shz2MnDfGDMMICJ/C/wa8Pn+j7C+2V5P2fKRxnHydeEDjWFWS8zZYglzd4eZve0g\nuG2DY6cH6L12MZ/iNf3pHVL3H5V8//WV1ecee3JxmYkPPmV9Oe6OsbmRrivnS27YU+ow2VUQXOnt\nt8PO8njoe/UKqdUE6dU1fOFQPh1h4Je+THJhiex6ikC0uaAV89bVY8u26X/jOtOf3mFlfBKMIdDS\nSHJxpXRdWyDY0Ur05FEampvyNXNLiU/NsHDvEZnkOg2tUQLNjdg+L21nB5n88Fb5NIxKg3GRfHvl\ncoHzo3/5Cad+/Wu0HD/C/D23skaJN2R57GnZtVBHG5ihysaQ4zYhKdG2GcA4rK+s0nP1JUZ/8kGu\nJrD7h6ehLUpyvvR/T9vrwRvS1V/1hfF7wP8o8fUeYHNbxTHgaqkXEJE/AP4AoLMhstfjqztiWURP\nHmXu7sPCTpyWRaSnI9+FtPXMSRJzC1uq27hlLTe+tjFrz90Zxh8J5VMkAk0RxGNjtjZyEiHQ/Hyp\nEOm1JI/fvVnQHCq5sMTID24w8LXXStaRV+qw2O1K8DvAt4wxGRH5M9zbb/9p98OqTb5QMF8KbIOI\n0BBtrvg1PH4f3VfOw5XzAKRWEzx850clj7W8Hvpeubzj5oLZ2/eZu/N0Yt34tI5tIUDLQD/ruZVa\nJ/Uct7JE8IWCeAJ+1soEkQAYw/jPPqb35QuUS8MwWacgB9gbDBA7fZzZz8u3eS4YSq4E2sPv/ahk\nxQjjGCyPje3zcfT166wvrZBKrOGPhDBZh0c/uFHydZ10hkxyPV/hQqlaJCLfAzpLPPVtY8zf5475\nNpABvrub9zLGfAf4DsCZlo7db2ZQxE4PYByH+Y2KQ8bQ2NdFx4WnGSkN0WZ6rl1k6qPPSa8lEdxq\nP8mVOBTd/coyd2c4HwRHejuZ/uwu2Wy2YH4UyyI2+HypZwvDj3FKLKKYrMPiozFip/QOmjq8dhUE\nV3j7TW3DFwq6t+7n5kuu1k5/epvYqYGCFebNMsl15m4Pl0k9cEuxLQ4/5sgvXqUh2sTdf/he6W53\nloU32EA6XuKWmTGk4qukSj23RXxskuzFNMG2KEuJRFGgKrZd9KGh9fQJkvNLxCdntn1tbzhIz5de\nwh8OEYy1kJgprkXsi4QKPqj4myL59syp1UTZlW+DKShRpFQtMsZ8dbvnReR3gW8Cb5rSZVDGgb5N\nj3tzX1MHQERoOztI7PQAmbV1bL+vZPOKcEcboa+9hpNKI7bNwsPHJG/dLfmamysFWbbN0dev8eT9\nT1ibW0QEvMEGOi+dw9/4fKkLyflFKJPCkVxcfq7XVKpW7GVOcLnbb6qM5NIKy6NP8IaDBDIZkguF\nt/iddIaF4cesjE9x7M1XS9YFTszMuxshtkmpNVmHhQcjNERfJNzVzvLok+LgFGh9YYCJD26VnPAq\nJkJyfonYqeOsjE3mu+ZBLie3MUwwt4M4ubhMYmYey+shOnhs2yA41NFK76ZV8a4rLzLygxtk02lM\nJot4bCzbpufqhbKv4QsF8YVDrC+vFI25IdqsneHUoZar+vDHwJeNMYkyh/0MOCkix3CD398G/vUB\nDVHlWLa94+ZbEcmnGgSaG9184hJpc4EtFR+8wQb6X7ua24jnlF1AqZQvEiYxu1C8gGBZ7kY8pQ6x\nHYPgvbr9pvllhWaH7jN3dzif4yW2jb+5kdRKvDAXzDFk11PM339IQ7TFTb9ojeY7/0gu5WGne5Xp\nNTc/t+3sIKtTszi5CXLjvVtfOMH60sruAmByXYm8HnyhIP1vXGPm1l1Wp2exbJum/h5aXzgBwPjN\nD4lPzmCMQcQCDJ5wkEy89N/uhrZoQVqItyHAwC+9RnximvXlOL5wkHB3x46ruT1XLzDyw5s42Swm\nm8Xy2FheD91XXtzVeStVA/4S8APv5H5Xbhhj/lBEunFLoX0jl7r2R8DbuCXS/toY81n1hlx/kovL\nJGbnsb1ewt0dFbUxDrZG8UVCpJZXCipMiG3l59Stdtse2Rjjbgb32G4r+q0LJyI0H+sr/c1KHRI7\n/pbswe23jdfR/LKc9aWVggAY3Nyu9TK3lozjMHd7ONe0ww15u7/0EuGudgItTTg7bG6TXKrDw3/+\nMamVOJbXS0NrFJPJ4An4aTrWhwDL45PbtieuhO3z5Vcm/JEwvdcvFR0z/2DEDYDzmzzc1Y1MmdJA\nAEvDo7QcO1IwsbsbSjqJ9FQ+Pl8kxMDXv8zK+BTp1QS+SJhId7u2R1aHnjGmZDRkjHkCfGPT438E\n/vGgxqVcxnEY/+nHrE7NYEyu5ORHn9N77SKhjtZtv1dEOPKLLzP9yRDLoxMYx8Hf1EjHhRcINDfu\n+VizqTSP3/0pqdVEvsoRGMS2AMH2eeh++YLuoVCH3m6rQ1Ry+01tsTw68WwlwXI2784du/khTX09\nLI8+cVtrbvN9YgkrY5P5ld/seoq1uXmajvTQ0NrC+I0Pc+05TcUBsNgWltfrbnIzBmwLy7LpvX5p\nx418iw8elzx/EYtgV4zViemi5zLJdRYfjT53XeHNLNum6Uj3rl9HKaUqtTD82A2At1R4GLvxISe+\n8caOK7e210PX5fN0XjoHsK/d2KY+/pz1ePzpPpXcQovt9dH76iX8jRHtBqe+EHabE1zy9tuuR/UF\n5zily6E924sYtzXwllVg2+fD1xhibX4JAUJdbawvrpBeLfyMsrGzd2lkHOM4z7T4K7ZNY28XHRfP\nsDa7QHJxGW+Dv6J0BKAgT7hgTMaU7R5nHIf4+NSeBMFKKXXQFsp8+EcgPjFd8Qfz/Q4+N1o5l9qo\nnU2nwez/GJQ6KLutDlE6GUltK9LVweLDsaJNDmJbhDvbiedWQg1sn6NbIg3CyWbovHAGf2Mkd4jh\nzt+9Xf4lKskBFnIrxW5uWnTwKKH21udulRlqb3UD+K1vI+ALB0kuLJZckbaeobudUkrVknIf/nEM\nTg11YTPGUC6zUURKVxdS6pDSjnFV0NDaQqijldWp2XwgLLaFNxik6/I5nEyWlSdTONksq1MzJKaL\nS4GVI2KRXl3LB8EiguXxlJ6AK+5aJ0QHj9N6emBPcmdbz5xgZWKqYDJ1PwC0ERs8xsr4ZNGKidi2\nbsJQSh1aofYYy6MTxU8IBNuiQG7/x92HLD4cxclkCLZGaTs3eKCd2dzKFWFSK/Gi54xxCLTsfQ6y\nUtWiu4GqQETouXqBrktnCbZGCbQ00XZ2kKNvXMPyePAE/DQf6yM5v0hitkRzCqFcLwqM4+DbUg+y\n+XhfyeDVyrXx3JExJGbn92zzmDfYwLE3X6WpvwdPwI8vEqLt3Cm6X76AvzFC25lB970sy207bVs0\nHekm3NW2J++vlFIHrfXMSSxP4bqT2Dbhro78osXYez9n7s4DMmtJnHSG+MQ0I99/r6Ia7Xup48IL\nuU1whWNtfaH4HJQ6zPR/c5WICI193TT2lc4DS8zOE5+cLZkO4W9qpKm/h5lbdwpWTMWyCHW0FnW1\naztzklQ8werkjFtTmFznui+9xOiPP6goJSKzlnyW09uRN9hA1+XzJZ+LnjxKpKfDXRF2DOHOtnzD\nC6WUOox8oSBH33yF2aH7rE7PYXs9tAz05+9wrc0vkZhdKLoL5mSzzH5+n+6XXzqwsYbaYvS/dpWZ\nofusLy7jaQgQOzVApLv9wMag1EHQILhGxcenShZGB7Asi+hAP5bHZubWXbLpNCJCU38v7edPFR0v\nlkXvtYuk4qskF5fxBPw0xNyaw0ffuMbUR0OsTs9uO55gLLon51Upb7CBqG6CU0p9gfhCwbI1ydfm\n5jGmxIKEcRdFDlqgpYm+Vy4f+PsqdZA0CK5V26QebNymau7vpelID046g+Wxd0xX8IVD+MKhoq/1\n/cIVHMdhYfgxM5/eKcoVFo9N7LT2h1dKqf1i+3xuV7gS1YMs7Wap1L7QnOAa1dTXVZSTBW5eVtPR\n3qePRbB93l3n61qWRezEUU5+802aj/Yitg0iBFuj9L92tSh4VkoptXfC3R0lvy62e+dPKbX3dCW4\nRgVammgZ6GfhwUhBa+VQe4zG3q59e1/b66Hz0rl8QXallFL7z/Z66Lt+mbH3PnArROaaFzX2dRcs\nfCil9o4GwTWs/dwpGns6WXr8BOM4RHo6CbZFtVC5Ukp9AQXbopz45a8Qn5zBSbsl0nzh4M7fqJR6\nLhoE17hASxOBlqZqD0MppdQBsGybxp7Oag9DqbqgOcFKKaWUUqruSLn2iPv6piIzwEgFh7YC29fu\n+mLT89fzr+fzh9q8Bv3GmLrq3LLDnF2LP6Nq0Wvh0uvwlF4LV7WvQ8l5uypBcKVE5H1jzJVqj6Na\n9Pz1/Ov5/EGvwWGgP6On9Fq49Do8pdfCVavXQdMhlFJKKaVU3dEgWCmllFJK1Z1aD4K/U+0BVJme\nf32r9/MHvQaHgf6MntJr4dLr8JReC1dNXoeazglWSimllFJqP9T6SrBSSimllFJ7ruaDYBH5cxG5\nLSKfiMjfiUhztcd0kETkX4nIZyLiiEjN7azcLyLylojcEZH7IvIn1R7PQRKRvxaRaRG5Ve2xVIOI\n9InI90Xk89z//X9f7TGp7dX7PL1Zvc7ZG+p57t6s3ufxDbU+n9d8EAy8A5wzxrwI3AW+VeXxHLRb\nwG8C71Z7IAdFRGzgr4CvA2eA3xGRM9Ud1YH6G+Ctag+iijLAfzTGnAGuAf+uzn7+h1G9z9Ob1d2c\nvUHn7gJ/Q33P4xtqej6v+SDYGPNPxphM7uENoLea4zloxpghY8ydao/jgL0M3DfGDBtjUsDfAr9W\n5TEdGGPMu8B8tcdRLcaYCWPMz3P/XgGGgJ7qjkptp97n6c3qdM7eUNdz92b1Po9vqPX5vOaD4C1+\nD/h/1R6E2nc9wOimx2PU0C+NOjgichS4CNys7kjUM9B5un7p3K3KqsX53FPtAQCIyPeAzhJPfdsY\n8/e5Y76Nu6z+3YMc20Go5PyVqjciEgb+F/AfjDHL1R5Pvav3eXoznbOVeja1Op/XRBBsjPnqds+L\nyO8C3wTeNF/Amm47nX8dGgf6Nj3uzX1N1QkR8eJOmN81xvzvao9H6Ty9mc7ZZencrYrU8nxe8+kQ\nIvIW8MfArxpjEtUejzoQPwNOisgxEfEBvw38nyqPSR0QERHgvwFDxpi/qPZ41M50nlY5OnerArU+\nn9d8EAz8JRAB3hGRj0Tkv1R7QAdJRH5DRMaA68D/FZG3qz2m/ZbbYPNHwNu4SfT/0xjzWXVHdXBE\n5L8D7wGnRGRMRH6/2mM6YK8C/wb4Su53/iMR+Ua1B6W2Vdfz9Gb1OGdvqPe5ezOdx/Nqej7XjnFK\nKaWUUqruHIaVYKWUUkoppfaUBsFKKaWUUqruaBCslFJKKaXqjgbBSimllFKq7mgQrJRSSiml6o4G\nwUoppZRSqu5oEKyUUkoppeqOBsFKKaWUUqru/H9G3r0cBZbd+wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Cv3ev9Uo1Ii",
        "colab_type": "text"
      },
      "source": [
        "# Inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h25pUbR8o9eF",
        "colab_type": "code",
        "outputId": "c9c3a9bc-c12c-42b4-a8a6-e2bcdf42d5d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "# Inputs for inference\n",
        "X_infer = pd.DataFrame([{'X1': 0.1, 'X2': 0.1}])\n",
        "X_infer.head()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    X1   X2\n",
              "0  0.1  0.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8G5lIYReo9l_",
        "colab_type": "code",
        "outputId": "3b9126d6-940a-4b8b-c847-6acbf087bc86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Standardize\n",
        "standardized_X_infer = X_scaler.transform(X_infer)\n",
        "print (standardized_X_infer)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.30945845 0.30761858]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uow5g2-1o9kF",
        "colab_type": "code",
        "outputId": "99b36e9e-642f-45de-8ee0-0dc0daeb3a23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Predict\n",
        "y_infer = model.predict(standardized_X_infer)\n",
        "_class = np.argmax(y_infer)\n",
        "print (f\"The probability that you have a class {classes[_class]} is {y_infer[0][_class]*100.0:.0f}%\")"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The probability that you have a class c1 is 97%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWv-RU46k2UL",
        "colab_type": "text"
      },
      "source": [
        "# Initializing weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hDPBE0sk2mJ",
        "colab_type": "text"
      },
      "source": [
        "So far we have been initializing weights with small random values and this isn't optimal for convergence during training. The objective is to have weights that are able to produce outputs that follow a similar distribution across all neurons. We can do this by enforcing weights to have unit variance prior the affine and non-linear operations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YxCm7FyRFRWK",
        "colab_type": "text"
      },
      "source": [
        "<img width=\"45\" src=\"http://bestanimations.com/HomeOffice/Lights/Bulbs/animated-light-bulb-gif-29.gif\" align=\"left\" vspace=\"20px\" hspace=\"10px\">\n",
        "\n",
        "A popular method is to apply [xavier initialization](http://andyljones.tumblr.com/post/110998971763/an-explanation-of-xavier-initialization), which essentially initializes the weights to allow the signal from the data to reach deep into the network. You may be wondering why we don't do this for every forward pass and that's a great question. We'll look at more advanced strategies that help with optimization like batch/layer normalization, etc. in future lessons. Meanwhile you can check out other initializers [here](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/initializers)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7l7tg7SIgPF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.initializers import glorot_normal"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5lUR__TKvqA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# MLP\n",
        "class MLP(Model):\n",
        "    def __init__(self, hidden_dim, num_classes):\n",
        "        super(MLP, self).__init__()\n",
        "        xavier_initializer = glorot_normal() # xavier glorot initiailization\n",
        "        self.fc1 = Dense(units=hidden_dim,\n",
        "                         kernel_initializer=xavier_initializer,\n",
        "                         activation='relu')\n",
        "        self.fc2 = Dense(units=num_classes, \n",
        "                         activation='softmax')\n",
        "        \n",
        "    def call(self, x_in, training=False):\n",
        "        \"\"\"Forward pass.\"\"\"\n",
        "        z = self.fc1(x_in)\n",
        "        y_pred = self.fc2(z)\n",
        "        return y_pred\n",
        "    \n",
        "    def sample(self, input_shape):\n",
        "        x_in = Input(shape=input_shape)\n",
        "        return Model(inputs=x_in, outputs=self.call(x_in)).summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAyjh3bieLjn",
        "colab_type": "text"
      },
      "source": [
        "# Dropout"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6kLwcBveLy5",
        "colab_type": "text"
      },
      "source": [
        "A great technique to overcome overfitting is to increase the size of your data but this isn't always an option. Fortuntely, there are methods like regularization and dropout that can help create a more robust model. \n",
        "\n",
        "Dropout is a technique (used only during training) that allows us to zero the outputs of neurons. We do this for `dropout_p`% of the total neurons in each layer and it changes every batch. Dropout prevents units from co-adapting too much to the data and acts as a sampling strategy since we drop a different set of neurons each time.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/basic_ml/06_Multilayer_Perceptron/dropout.png\" width=\"350\">\n",
        "\n",
        "* [Dropout: A Simple Way to Prevent Neural Networks from\n",
        "Overfitting](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HT75Tnn-EVBM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.regularizers import l2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yu1E-FqbWT9b",
        "colab_type": "text"
      },
      "source": [
        "### Components"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6NvhBUyf27y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# MLP\n",
        "class MLP(Model):\n",
        "    def __init__(self, hidden_dim, lambda_l2, dropout_p, num_classes):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc1 = Dense(units=hidden_dim,\n",
        "                         kernel_regularizer=l2(lambda_l2), # adding L2 regularization\n",
        "                         activation='relu')\n",
        "        self.dropout = Dropout(rate=dropout_p)\n",
        "        self.fc2 = Dense(units=num_classes, \n",
        "                         activation='softmax')\n",
        "        \n",
        "    def call(self, x_in, training=False):\n",
        "        \"\"\"Forward pass.\"\"\"\n",
        "        z = self.fc1(x_in)\n",
        "        if training:\n",
        "            z = self.dropout(z, training=training) # adding dropout\n",
        "        y_pred = self.fc2(z)\n",
        "        return y_pred\n",
        "    \n",
        "    def sample(self, input_shape):\n",
        "        x_in = Input(shape=input_shape)\n",
        "        return Model(inputs=x_in, outputs=self.call(x_in)).summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BldBdTZGWUBM",
        "colab_type": "text"
      },
      "source": [
        "### Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGQq0MvcgBEG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Arguments\n",
        "DROPOUT_P = 0.1 # % of the neurons that are dropped each pass\n",
        "LAMBDA_L2 = 1e-4 # L2 regularization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQK9h7BNf26K",
        "colab_type": "code",
        "outputId": "c6f78ceb-637b-4b95-b0aa-b302392b0cb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Initialize the model\n",
        "model = MLP(hidden_dim=HIDDEN_DIM,\n",
        "            lambda_l2=LAMBDA_L2,\n",
        "            dropout_p=DROPOUT_P,\n",
        "            num_classes=NUM_CLASSES)\n",
        "model.sample(input_shape=(INPUT_DIM,))"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         [(None, 2)]               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 100)               300       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 3)                 303       \n",
            "=================================================================\n",
            "Total params: 603\n",
            "Trainable params: 603\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ijvHwcZg8mO",
        "colab_type": "text"
      },
      "source": [
        "# Overfitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIhvdD_zg8os",
        "colab_type": "text"
      },
      "source": [
        "Though neural networks are great at capturing non-linear relationships they are highly susceptible to overfitting to the training data and failing to generalize on test data. Just take a look at the example below where we generate completely random data and are able to fit a model with [$2*N*C + D$](https://arxiv.org/abs/1611.03530) hidden units. The training performance is good (~70%) but the overfitting leads to very poor test performance. We'll be covering strategies to tackle overfitting in future lessons."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRdM16NhazJP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Arguments\n",
        "NUM_EPOCHS = 500\n",
        "NUM_SAMPLES_PER_CLASS = 50\n",
        "LEARNING_RATE = 1e-1\n",
        "HIDDEN_DIM = 2 * NUM_SAMPLES_PER_CLASS * NUM_CLASSES + INPUT_DIM # 2*N*C + D"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qf00Biq6g8ty",
        "colab_type": "code",
        "outputId": "a0c07bd2-9343-4197-c475-1a4ac38f1e47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Generate random data\n",
        "X = np.random.rand(NUM_SAMPLES_PER_CLASS * NUM_CLASSES, INPUT_DIM)\n",
        "y = np.array([[i]*NUM_SAMPLES_PER_CLASS for i in range(NUM_CLASSES)]).reshape(-1)\n",
        "print (\"X: \", format(np.shape(X)))\n",
        "print (\"y: \", format(np.shape(y)))"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X:  (150, 2)\n",
            "y:  (150,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-bA9vK9SWkjh",
        "colab_type": "code",
        "outputId": "acc1bbb7-3bec-4efb-e1bf-eebfb7b6ef23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Create data splits\n",
        "X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n",
        "    X, y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n",
        "print (\"X_train:\", X_train.shape)\n",
        "print (\"y_train:\", y_train.shape)\n",
        "print (\"X_val:\", X_val.shape)\n",
        "print (\"y_val:\", y_val.shape)\n",
        "print (\"X_test:\", X_test.shape)\n",
        "print (\"y_test:\", y_test.shape)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train: (107, 2)\n",
            "y_train: (107,)\n",
            "X_val: (20, 2)\n",
            "y_val: (20,)\n",
            "X_test: (23, 2)\n",
            "y_test: (23,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYwc9mk1f5bm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Standardize the inputs (mean=0, std=1) using training data\n",
        "X_scaler = StandardScaler().fit(X_train)\n",
        "# Apply scaler on training and test data (don't standardize outputs for classification)\n",
        "standardized_X_train = X_scaler.transform(X_train)\n",
        "standardized_X_val = X_scaler.transform(X_val)\n",
        "standardized_X_test = X_scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xozz2bBoWkmq",
        "colab_type": "code",
        "outputId": "0799befa-7ce6-4aea-fddb-580f390eb8c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Initialize the model\n",
        "model = MLP(hidden_dim=HIDDEN_DIM,\n",
        "            lambda_l2=0.0,\n",
        "            dropout_p=0.0,\n",
        "            num_classes=NUM_CLASSES)\n",
        "model.sample(input_shape=(INPUT_DIM,))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         [(None, 2)]               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 302)               906       \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 3)                 909       \n",
            "=================================================================\n",
            "Total params: 1,815\n",
            "Trainable params: 1,815\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXnkWoPaWkpe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Compile\n",
        "optimizer = Adam(lr=LEARNING_RATE)\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss=SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgayj4E1WksH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "de060320-fc12-4bc5-e923-2af750ff30cb"
      },
      "source": [
        "# Training\n",
        "model.fit(x=standardized_X_train, \n",
        "          y=y_train,\n",
        "          validation_data=(standardized_X_val, y_val),\n",
        "          epochs=NUM_EPOCHS,\n",
        "          batch_size=BATCH_SIZE,\n",
        "          class_weight=class_weights,\n",
        "          shuffle=False,\n",
        "          verbose=1)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 107 samples, validate on 20 samples\n",
            "Epoch 1/500\n",
            "107/107 [==============================] - 1s 8ms/sample - loss: 0.0047 - accuracy: 0.2150 - val_loss: 0.0034 - val_accuracy: 0.3000\n",
            "Epoch 2/500\n",
            "107/107 [==============================] - 0s 362us/sample - loss: 0.0033 - accuracy: 0.3458 - val_loss: 0.0030 - val_accuracy: 0.3500\n",
            "Epoch 3/500\n",
            "107/107 [==============================] - 0s 341us/sample - loss: 0.0032 - accuracy: 0.4393 - val_loss: 0.0030 - val_accuracy: 0.4500\n",
            "Epoch 4/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0030 - accuracy: 0.4860 - val_loss: 0.0032 - val_accuracy: 0.3500\n",
            "Epoch 5/500\n",
            "107/107 [==============================] - 0s 315us/sample - loss: 0.0030 - accuracy: 0.4299 - val_loss: 0.0029 - val_accuracy: 0.4500\n",
            "Epoch 6/500\n",
            "107/107 [==============================] - 0s 549us/sample - loss: 0.0030 - accuracy: 0.4486 - val_loss: 0.0032 - val_accuracy: 0.3000\n",
            "Epoch 7/500\n",
            "107/107 [==============================] - 0s 323us/sample - loss: 0.0029 - accuracy: 0.4953 - val_loss: 0.0032 - val_accuracy: 0.3500\n",
            "Epoch 8/500\n",
            "107/107 [==============================] - 0s 377us/sample - loss: 0.0031 - accuracy: 0.5047 - val_loss: 0.0031 - val_accuracy: 0.4000\n",
            "Epoch 9/500\n",
            "107/107 [==============================] - 0s 347us/sample - loss: 0.0028 - accuracy: 0.5327 - val_loss: 0.0032 - val_accuracy: 0.4500\n",
            "Epoch 10/500\n",
            "107/107 [==============================] - 0s 327us/sample - loss: 0.0029 - accuracy: 0.4953 - val_loss: 0.0033 - val_accuracy: 0.4500\n",
            "Epoch 11/500\n",
            "107/107 [==============================] - 0s 345us/sample - loss: 0.0028 - accuracy: 0.5421 - val_loss: 0.0031 - val_accuracy: 0.4500\n",
            "Epoch 12/500\n",
            "107/107 [==============================] - 0s 350us/sample - loss: 0.0028 - accuracy: 0.5421 - val_loss: 0.0034 - val_accuracy: 0.4500\n",
            "Epoch 13/500\n",
            "107/107 [==============================] - 0s 340us/sample - loss: 0.0028 - accuracy: 0.5234 - val_loss: 0.0029 - val_accuracy: 0.5000\n",
            "Epoch 14/500\n",
            "107/107 [==============================] - 0s 372us/sample - loss: 0.0030 - accuracy: 0.4673 - val_loss: 0.0036 - val_accuracy: 0.3500\n",
            "Epoch 15/500\n",
            "107/107 [==============================] - 0s 371us/sample - loss: 0.0030 - accuracy: 0.4953 - val_loss: 0.0029 - val_accuracy: 0.5000\n",
            "Epoch 16/500\n",
            "107/107 [==============================] - 0s 347us/sample - loss: 0.0031 - accuracy: 0.4860 - val_loss: 0.0030 - val_accuracy: 0.4500\n",
            "Epoch 17/500\n",
            "107/107 [==============================] - 0s 338us/sample - loss: 0.0028 - accuracy: 0.4766 - val_loss: 0.0035 - val_accuracy: 0.4500\n",
            "Epoch 18/500\n",
            "107/107 [==============================] - 0s 348us/sample - loss: 0.0029 - accuracy: 0.4953 - val_loss: 0.0029 - val_accuracy: 0.4500\n",
            "Epoch 19/500\n",
            "107/107 [==============================] - 0s 351us/sample - loss: 0.0027 - accuracy: 0.5607 - val_loss: 0.0031 - val_accuracy: 0.6000\n",
            "Epoch 20/500\n",
            "107/107 [==============================] - 0s 331us/sample - loss: 0.0027 - accuracy: 0.5140 - val_loss: 0.0030 - val_accuracy: 0.5500\n",
            "Epoch 21/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0027 - accuracy: 0.5981 - val_loss: 0.0029 - val_accuracy: 0.6500\n",
            "Epoch 22/500\n",
            "107/107 [==============================] - 0s 314us/sample - loss: 0.0027 - accuracy: 0.5234 - val_loss: 0.0032 - val_accuracy: 0.4000\n",
            "Epoch 23/500\n",
            "107/107 [==============================] - 0s 311us/sample - loss: 0.0027 - accuracy: 0.5327 - val_loss: 0.0030 - val_accuracy: 0.5500\n",
            "Epoch 24/500\n",
            "107/107 [==============================] - 0s 294us/sample - loss: 0.0029 - accuracy: 0.5607 - val_loss: 0.0033 - val_accuracy: 0.3500\n",
            "Epoch 25/500\n",
            "107/107 [==============================] - 0s 322us/sample - loss: 0.0028 - accuracy: 0.4953 - val_loss: 0.0029 - val_accuracy: 0.6500\n",
            "Epoch 26/500\n",
            "107/107 [==============================] - 0s 400us/sample - loss: 0.0029 - accuracy: 0.5514 - val_loss: 0.0032 - val_accuracy: 0.4500\n",
            "Epoch 27/500\n",
            "107/107 [==============================] - 0s 313us/sample - loss: 0.0027 - accuracy: 0.5234 - val_loss: 0.0029 - val_accuracy: 0.6000\n",
            "Epoch 28/500\n",
            "107/107 [==============================] - 0s 308us/sample - loss: 0.0027 - accuracy: 0.5701 - val_loss: 0.0031 - val_accuracy: 0.6000\n",
            "Epoch 29/500\n",
            "107/107 [==============================] - 0s 273us/sample - loss: 0.0026 - accuracy: 0.5701 - val_loss: 0.0029 - val_accuracy: 0.6000\n",
            "Epoch 30/500\n",
            "107/107 [==============================] - 0s 337us/sample - loss: 0.0027 - accuracy: 0.5607 - val_loss: 0.0032 - val_accuracy: 0.4500\n",
            "Epoch 31/500\n",
            "107/107 [==============================] - 0s 337us/sample - loss: 0.0026 - accuracy: 0.5607 - val_loss: 0.0029 - val_accuracy: 0.6500\n",
            "Epoch 32/500\n",
            "107/107 [==============================] - 0s 304us/sample - loss: 0.0026 - accuracy: 0.5607 - val_loss: 0.0033 - val_accuracy: 0.4500\n",
            "Epoch 33/500\n",
            "107/107 [==============================] - 0s 313us/sample - loss: 0.0026 - accuracy: 0.5514 - val_loss: 0.0029 - val_accuracy: 0.6500\n",
            "Epoch 34/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0027 - accuracy: 0.5701 - val_loss: 0.0033 - val_accuracy: 0.4500\n",
            "Epoch 35/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0026 - accuracy: 0.5327 - val_loss: 0.0030 - val_accuracy: 0.7000\n",
            "Epoch 36/500\n",
            "107/107 [==============================] - 0s 306us/sample - loss: 0.0028 - accuracy: 0.5794 - val_loss: 0.0033 - val_accuracy: 0.5000\n",
            "Epoch 37/500\n",
            "107/107 [==============================] - 0s 306us/sample - loss: 0.0026 - accuracy: 0.5234 - val_loss: 0.0030 - val_accuracy: 0.6000\n",
            "Epoch 38/500\n",
            "107/107 [==============================] - 0s 280us/sample - loss: 0.0027 - accuracy: 0.5794 - val_loss: 0.0033 - val_accuracy: 0.6000\n",
            "Epoch 39/500\n",
            "107/107 [==============================] - 0s 290us/sample - loss: 0.0026 - accuracy: 0.5701 - val_loss: 0.0031 - val_accuracy: 0.5000\n",
            "Epoch 40/500\n",
            "107/107 [==============================] - 0s 290us/sample - loss: 0.0027 - accuracy: 0.5514 - val_loss: 0.0035 - val_accuracy: 0.5500\n",
            "Epoch 41/500\n",
            "107/107 [==============================] - 0s 292us/sample - loss: 0.0027 - accuracy: 0.5514 - val_loss: 0.0030 - val_accuracy: 0.5500\n",
            "Epoch 42/500\n",
            "107/107 [==============================] - 0s 327us/sample - loss: 0.0028 - accuracy: 0.5514 - val_loss: 0.0034 - val_accuracy: 0.5000\n",
            "Epoch 43/500\n",
            "107/107 [==============================] - 0s 310us/sample - loss: 0.0026 - accuracy: 0.5514 - val_loss: 0.0030 - val_accuracy: 0.6500\n",
            "Epoch 44/500\n",
            "107/107 [==============================] - 0s 313us/sample - loss: 0.0026 - accuracy: 0.5514 - val_loss: 0.0034 - val_accuracy: 0.5000\n",
            "Epoch 45/500\n",
            "107/107 [==============================] - 0s 311us/sample - loss: 0.0025 - accuracy: 0.5607 - val_loss: 0.0031 - val_accuracy: 0.6500\n",
            "Epoch 46/500\n",
            "107/107 [==============================] - 0s 296us/sample - loss: 0.0026 - accuracy: 0.5701 - val_loss: 0.0034 - val_accuracy: 0.6000\n",
            "Epoch 47/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0026 - accuracy: 0.5607 - val_loss: 0.0032 - val_accuracy: 0.5500\n",
            "Epoch 48/500\n",
            "107/107 [==============================] - 0s 299us/sample - loss: 0.0028 - accuracy: 0.5421 - val_loss: 0.0035 - val_accuracy: 0.5000\n",
            "Epoch 49/500\n",
            "107/107 [==============================] - 0s 326us/sample - loss: 0.0026 - accuracy: 0.5421 - val_loss: 0.0030 - val_accuracy: 0.6000\n",
            "Epoch 50/500\n",
            "107/107 [==============================] - 0s 304us/sample - loss: 0.0027 - accuracy: 0.5607 - val_loss: 0.0035 - val_accuracy: 0.5000\n",
            "Epoch 51/500\n",
            "107/107 [==============================] - 0s 281us/sample - loss: 0.0025 - accuracy: 0.5701 - val_loss: 0.0031 - val_accuracy: 0.6500\n",
            "Epoch 52/500\n",
            "107/107 [==============================] - 0s 294us/sample - loss: 0.0026 - accuracy: 0.5514 - val_loss: 0.0035 - val_accuracy: 0.4500\n",
            "Epoch 53/500\n",
            "107/107 [==============================] - 0s 305us/sample - loss: 0.0025 - accuracy: 0.5514 - val_loss: 0.0031 - val_accuracy: 0.6500\n",
            "Epoch 54/500\n",
            "107/107 [==============================] - 0s 360us/sample - loss: 0.0026 - accuracy: 0.5701 - val_loss: 0.0033 - val_accuracy: 0.6000\n",
            "Epoch 55/500\n",
            "107/107 [==============================] - 0s 378us/sample - loss: 0.0025 - accuracy: 0.5514 - val_loss: 0.0032 - val_accuracy: 0.6000\n",
            "Epoch 56/500\n",
            "107/107 [==============================] - 0s 335us/sample - loss: 0.0027 - accuracy: 0.5421 - val_loss: 0.0033 - val_accuracy: 0.5500\n",
            "Epoch 57/500\n",
            "107/107 [==============================] - 0s 330us/sample - loss: 0.0026 - accuracy: 0.5514 - val_loss: 0.0032 - val_accuracy: 0.6000\n",
            "Epoch 58/500\n",
            "107/107 [==============================] - 0s 326us/sample - loss: 0.0027 - accuracy: 0.5701 - val_loss: 0.0035 - val_accuracy: 0.5500\n",
            "Epoch 59/500\n",
            "107/107 [==============================] - 0s 359us/sample - loss: 0.0026 - accuracy: 0.5607 - val_loss: 0.0032 - val_accuracy: 0.6000\n",
            "Epoch 60/500\n",
            "107/107 [==============================] - 0s 372us/sample - loss: 0.0026 - accuracy: 0.5421 - val_loss: 0.0034 - val_accuracy: 0.6000\n",
            "Epoch 61/500\n",
            "107/107 [==============================] - 0s 378us/sample - loss: 0.0025 - accuracy: 0.5607 - val_loss: 0.0032 - val_accuracy: 0.7000\n",
            "Epoch 62/500\n",
            "107/107 [==============================] - 0s 326us/sample - loss: 0.0026 - accuracy: 0.5607 - val_loss: 0.0034 - val_accuracy: 0.5500\n",
            "Epoch 63/500\n",
            "107/107 [==============================] - 0s 387us/sample - loss: 0.0025 - accuracy: 0.5514 - val_loss: 0.0034 - val_accuracy: 0.5500\n",
            "Epoch 64/500\n",
            "107/107 [==============================] - 0s 334us/sample - loss: 0.0027 - accuracy: 0.5327 - val_loss: 0.0036 - val_accuracy: 0.6500\n",
            "Epoch 65/500\n",
            "107/107 [==============================] - 0s 333us/sample - loss: 0.0026 - accuracy: 0.5701 - val_loss: 0.0031 - val_accuracy: 0.6500\n",
            "Epoch 66/500\n",
            "107/107 [==============================] - 0s 333us/sample - loss: 0.0026 - accuracy: 0.5888 - val_loss: 0.0036 - val_accuracy: 0.6000\n",
            "Epoch 67/500\n",
            "107/107 [==============================] - 0s 372us/sample - loss: 0.0025 - accuracy: 0.5421 - val_loss: 0.0029 - val_accuracy: 0.7000\n",
            "Epoch 68/500\n",
            "107/107 [==============================] - 0s 324us/sample - loss: 0.0025 - accuracy: 0.5794 - val_loss: 0.0037 - val_accuracy: 0.4500\n",
            "Epoch 69/500\n",
            "107/107 [==============================] - 0s 341us/sample - loss: 0.0025 - accuracy: 0.5701 - val_loss: 0.0029 - val_accuracy: 0.7000\n",
            "Epoch 70/500\n",
            "107/107 [==============================] - 0s 316us/sample - loss: 0.0025 - accuracy: 0.6168 - val_loss: 0.0036 - val_accuracy: 0.4000\n",
            "Epoch 71/500\n",
            "107/107 [==============================] - 0s 301us/sample - loss: 0.0025 - accuracy: 0.5701 - val_loss: 0.0032 - val_accuracy: 0.7000\n",
            "Epoch 72/500\n",
            "107/107 [==============================] - 0s 329us/sample - loss: 0.0025 - accuracy: 0.6168 - val_loss: 0.0038 - val_accuracy: 0.4500\n",
            "Epoch 73/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0025 - accuracy: 0.5607 - val_loss: 0.0032 - val_accuracy: 0.6500\n",
            "Epoch 74/500\n",
            "107/107 [==============================] - 0s 310us/sample - loss: 0.0027 - accuracy: 0.5981 - val_loss: 0.0036 - val_accuracy: 0.5500\n",
            "Epoch 75/500\n",
            "107/107 [==============================] - 0s 303us/sample - loss: 0.0025 - accuracy: 0.5701 - val_loss: 0.0033 - val_accuracy: 0.6000\n",
            "Epoch 76/500\n",
            "107/107 [==============================] - 0s 319us/sample - loss: 0.0025 - accuracy: 0.5607 - val_loss: 0.0037 - val_accuracy: 0.6000\n",
            "Epoch 77/500\n",
            "107/107 [==============================] - 0s 306us/sample - loss: 0.0025 - accuracy: 0.5794 - val_loss: 0.0033 - val_accuracy: 0.6500\n",
            "Epoch 78/500\n",
            "107/107 [==============================] - 0s 311us/sample - loss: 0.0025 - accuracy: 0.5701 - val_loss: 0.0035 - val_accuracy: 0.6000\n",
            "Epoch 79/500\n",
            "107/107 [==============================] - 0s 323us/sample - loss: 0.0024 - accuracy: 0.5981 - val_loss: 0.0035 - val_accuracy: 0.6500\n",
            "Epoch 80/500\n",
            "107/107 [==============================] - 0s 290us/sample - loss: 0.0025 - accuracy: 0.5701 - val_loss: 0.0037 - val_accuracy: 0.5500\n",
            "Epoch 81/500\n",
            "107/107 [==============================] - 0s 293us/sample - loss: 0.0025 - accuracy: 0.5794 - val_loss: 0.0034 - val_accuracy: 0.6500\n",
            "Epoch 82/500\n",
            "107/107 [==============================] - 0s 435us/sample - loss: 0.0027 - accuracy: 0.5607 - val_loss: 0.0033 - val_accuracy: 0.6000\n",
            "Epoch 83/500\n",
            "107/107 [==============================] - 0s 352us/sample - loss: 0.0025 - accuracy: 0.5607 - val_loss: 0.0037 - val_accuracy: 0.5500\n",
            "Epoch 84/500\n",
            "107/107 [==============================] - 0s 311us/sample - loss: 0.0025 - accuracy: 0.5888 - val_loss: 0.0039 - val_accuracy: 0.7000\n",
            "Epoch 85/500\n",
            "107/107 [==============================] - 0s 389us/sample - loss: 0.0024 - accuracy: 0.5514 - val_loss: 0.0034 - val_accuracy: 0.6000\n",
            "Epoch 86/500\n",
            "107/107 [==============================] - 0s 383us/sample - loss: 0.0025 - accuracy: 0.5888 - val_loss: 0.0038 - val_accuracy: 0.6000\n",
            "Epoch 87/500\n",
            "107/107 [==============================] - 0s 342us/sample - loss: 0.0024 - accuracy: 0.5981 - val_loss: 0.0036 - val_accuracy: 0.7000\n",
            "Epoch 88/500\n",
            "107/107 [==============================] - 0s 349us/sample - loss: 0.0025 - accuracy: 0.5794 - val_loss: 0.0036 - val_accuracy: 0.5000\n",
            "Epoch 89/500\n",
            "107/107 [==============================] - 0s 326us/sample - loss: 0.0025 - accuracy: 0.5888 - val_loss: 0.0034 - val_accuracy: 0.7000\n",
            "Epoch 90/500\n",
            "107/107 [==============================] - 0s 332us/sample - loss: 0.0025 - accuracy: 0.6355 - val_loss: 0.0037 - val_accuracy: 0.5500\n",
            "Epoch 91/500\n",
            "107/107 [==============================] - 0s 325us/sample - loss: 0.0024 - accuracy: 0.5607 - val_loss: 0.0035 - val_accuracy: 0.5000\n",
            "Epoch 92/500\n",
            "107/107 [==============================] - 0s 333us/sample - loss: 0.0025 - accuracy: 0.5701 - val_loss: 0.0038 - val_accuracy: 0.6000\n",
            "Epoch 93/500\n",
            "107/107 [==============================] - 0s 340us/sample - loss: 0.0025 - accuracy: 0.5981 - val_loss: 0.0034 - val_accuracy: 0.7000\n",
            "Epoch 94/500\n",
            "107/107 [==============================] - 0s 340us/sample - loss: 0.0025 - accuracy: 0.5794 - val_loss: 0.0038 - val_accuracy: 0.6500\n",
            "Epoch 95/500\n",
            "107/107 [==============================] - 0s 324us/sample - loss: 0.0025 - accuracy: 0.5888 - val_loss: 0.0033 - val_accuracy: 0.7500\n",
            "Epoch 96/500\n",
            "107/107 [==============================] - 0s 356us/sample - loss: 0.0024 - accuracy: 0.6355 - val_loss: 0.0036 - val_accuracy: 0.5500\n",
            "Epoch 97/500\n",
            "107/107 [==============================] - 0s 329us/sample - loss: 0.0023 - accuracy: 0.5888 - val_loss: 0.0036 - val_accuracy: 0.7000\n",
            "Epoch 98/500\n",
            "107/107 [==============================] - 0s 324us/sample - loss: 0.0023 - accuracy: 0.6168 - val_loss: 0.0036 - val_accuracy: 0.7000\n",
            "Epoch 99/500\n",
            "107/107 [==============================] - 0s 327us/sample - loss: 0.0022 - accuracy: 0.5981 - val_loss: 0.0036 - val_accuracy: 0.7000\n",
            "Epoch 100/500\n",
            "107/107 [==============================] - 0s 331us/sample - loss: 0.0023 - accuracy: 0.5981 - val_loss: 0.0035 - val_accuracy: 0.7000\n",
            "Epoch 101/500\n",
            "107/107 [==============================] - 0s 325us/sample - loss: 0.0022 - accuracy: 0.6262 - val_loss: 0.0038 - val_accuracy: 0.7500\n",
            "Epoch 102/500\n",
            "107/107 [==============================] - 0s 322us/sample - loss: 0.0025 - accuracy: 0.6075 - val_loss: 0.0038 - val_accuracy: 0.4000\n",
            "Epoch 103/500\n",
            "107/107 [==============================] - 0s 328us/sample - loss: 0.0024 - accuracy: 0.6075 - val_loss: 0.0045 - val_accuracy: 0.6500\n",
            "Epoch 104/500\n",
            "107/107 [==============================] - 0s 310us/sample - loss: 0.0026 - accuracy: 0.5327 - val_loss: 0.0038 - val_accuracy: 0.5000\n",
            "Epoch 105/500\n",
            "107/107 [==============================] - 0s 286us/sample - loss: 0.0024 - accuracy: 0.5794 - val_loss: 0.0043 - val_accuracy: 0.6500\n",
            "Epoch 106/500\n",
            "107/107 [==============================] - 0s 276us/sample - loss: 0.0027 - accuracy: 0.5607 - val_loss: 0.0041 - val_accuracy: 0.7000\n",
            "Epoch 107/500\n",
            "107/107 [==============================] - 0s 277us/sample - loss: 0.0025 - accuracy: 0.5607 - val_loss: 0.0041 - val_accuracy: 0.4500\n",
            "Epoch 108/500\n",
            "107/107 [==============================] - 0s 289us/sample - loss: 0.0028 - accuracy: 0.6262 - val_loss: 0.0046 - val_accuracy: 0.6500\n",
            "Epoch 109/500\n",
            "107/107 [==============================] - 0s 391us/sample - loss: 0.0026 - accuracy: 0.5421 - val_loss: 0.0033 - val_accuracy: 0.5500\n",
            "Epoch 110/500\n",
            "107/107 [==============================] - 0s 343us/sample - loss: 0.0025 - accuracy: 0.5514 - val_loss: 0.0050 - val_accuracy: 0.6500\n",
            "Epoch 111/500\n",
            "107/107 [==============================] - 0s 298us/sample - loss: 0.0028 - accuracy: 0.5981 - val_loss: 0.0037 - val_accuracy: 0.6500\n",
            "Epoch 112/500\n",
            "107/107 [==============================] - 0s 306us/sample - loss: 0.0027 - accuracy: 0.5514 - val_loss: 0.0050 - val_accuracy: 0.4000\n",
            "Epoch 113/500\n",
            "107/107 [==============================] - 0s 304us/sample - loss: 0.0028 - accuracy: 0.5607 - val_loss: 0.0045 - val_accuracy: 0.6500\n",
            "Epoch 114/500\n",
            "107/107 [==============================] - 0s 303us/sample - loss: 0.0029 - accuracy: 0.5421 - val_loss: 0.0039 - val_accuracy: 0.6000\n",
            "Epoch 115/500\n",
            "107/107 [==============================] - 0s 323us/sample - loss: 0.0023 - accuracy: 0.6262 - val_loss: 0.0040 - val_accuracy: 0.5000\n",
            "Epoch 116/500\n",
            "107/107 [==============================] - 0s 324us/sample - loss: 0.0025 - accuracy: 0.5701 - val_loss: 0.0029 - val_accuracy: 0.6500\n",
            "Epoch 117/500\n",
            "107/107 [==============================] - 0s 299us/sample - loss: 0.0023 - accuracy: 0.6355 - val_loss: 0.0047 - val_accuracy: 0.6000\n",
            "Epoch 118/500\n",
            "107/107 [==============================] - 0s 319us/sample - loss: 0.0027 - accuracy: 0.5514 - val_loss: 0.0036 - val_accuracy: 0.5500\n",
            "Epoch 119/500\n",
            "107/107 [==============================] - 0s 275us/sample - loss: 0.0024 - accuracy: 0.6449 - val_loss: 0.0036 - val_accuracy: 0.6000\n",
            "Epoch 120/500\n",
            "107/107 [==============================] - 0s 305us/sample - loss: 0.0025 - accuracy: 0.6262 - val_loss: 0.0034 - val_accuracy: 0.5000\n",
            "Epoch 121/500\n",
            "107/107 [==============================] - 0s 285us/sample - loss: 0.0024 - accuracy: 0.5794 - val_loss: 0.0035 - val_accuracy: 0.7500\n",
            "Epoch 122/500\n",
            "107/107 [==============================] - 0s 297us/sample - loss: 0.0024 - accuracy: 0.5981 - val_loss: 0.0042 - val_accuracy: 0.6000\n",
            "Epoch 123/500\n",
            "107/107 [==============================] - 0s 270us/sample - loss: 0.0025 - accuracy: 0.6075 - val_loss: 0.0033 - val_accuracy: 0.7000\n",
            "Epoch 124/500\n",
            "107/107 [==============================] - 0s 303us/sample - loss: 0.0023 - accuracy: 0.6449 - val_loss: 0.0037 - val_accuracy: 0.5000\n",
            "Epoch 125/500\n",
            "107/107 [==============================] - 0s 338us/sample - loss: 0.0023 - accuracy: 0.6355 - val_loss: 0.0034 - val_accuracy: 0.7500\n",
            "Epoch 126/500\n",
            "107/107 [==============================] - 0s 290us/sample - loss: 0.0024 - accuracy: 0.5981 - val_loss: 0.0041 - val_accuracy: 0.6000\n",
            "Epoch 127/500\n",
            "107/107 [==============================] - 0s 317us/sample - loss: 0.0023 - accuracy: 0.6075 - val_loss: 0.0035 - val_accuracy: 0.7000\n",
            "Epoch 128/500\n",
            "107/107 [==============================] - 0s 321us/sample - loss: 0.0023 - accuracy: 0.6449 - val_loss: 0.0042 - val_accuracy: 0.5000\n",
            "Epoch 129/500\n",
            "107/107 [==============================] - 0s 310us/sample - loss: 0.0023 - accuracy: 0.5514 - val_loss: 0.0030 - val_accuracy: 0.7500\n",
            "Epoch 130/500\n",
            "107/107 [==============================] - 0s 293us/sample - loss: 0.0023 - accuracy: 0.6075 - val_loss: 0.0042 - val_accuracy: 0.6500\n",
            "Epoch 131/500\n",
            "107/107 [==============================] - 0s 338us/sample - loss: 0.0023 - accuracy: 0.5981 - val_loss: 0.0029 - val_accuracy: 0.7000\n",
            "Epoch 132/500\n",
            "107/107 [==============================] - 0s 316us/sample - loss: 0.0023 - accuracy: 0.6729 - val_loss: 0.0041 - val_accuracy: 0.6000\n",
            "Epoch 133/500\n",
            "107/107 [==============================] - 0s 305us/sample - loss: 0.0023 - accuracy: 0.6168 - val_loss: 0.0030 - val_accuracy: 0.7500\n",
            "Epoch 134/500\n",
            "107/107 [==============================] - 0s 281us/sample - loss: 0.0023 - accuracy: 0.5794 - val_loss: 0.0044 - val_accuracy: 0.7000\n",
            "Epoch 135/500\n",
            "107/107 [==============================] - 0s 303us/sample - loss: 0.0023 - accuracy: 0.6542 - val_loss: 0.0032 - val_accuracy: 0.5000\n",
            "Epoch 136/500\n",
            "107/107 [==============================] - 0s 299us/sample - loss: 0.0025 - accuracy: 0.5888 - val_loss: 0.0043 - val_accuracy: 0.6500\n",
            "Epoch 137/500\n",
            "107/107 [==============================] - 0s 332us/sample - loss: 0.0023 - accuracy: 0.6168 - val_loss: 0.0038 - val_accuracy: 0.6500\n",
            "Epoch 138/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0023 - accuracy: 0.5981 - val_loss: 0.0043 - val_accuracy: 0.7500\n",
            "Epoch 139/500\n",
            "107/107 [==============================] - 0s 343us/sample - loss: 0.0023 - accuracy: 0.6729 - val_loss: 0.0042 - val_accuracy: 0.6500\n",
            "Epoch 140/500\n",
            "107/107 [==============================] - 0s 324us/sample - loss: 0.0024 - accuracy: 0.5701 - val_loss: 0.0038 - val_accuracy: 0.6000\n",
            "Epoch 141/500\n",
            "107/107 [==============================] - 0s 294us/sample - loss: 0.0023 - accuracy: 0.6449 - val_loss: 0.0049 - val_accuracy: 0.6500\n",
            "Epoch 142/500\n",
            "107/107 [==============================] - 0s 335us/sample - loss: 0.0025 - accuracy: 0.5514 - val_loss: 0.0037 - val_accuracy: 0.5500\n",
            "Epoch 143/500\n",
            "107/107 [==============================] - 0s 345us/sample - loss: 0.0023 - accuracy: 0.6262 - val_loss: 0.0039 - val_accuracy: 0.7000\n",
            "Epoch 144/500\n",
            "107/107 [==============================] - 0s 271us/sample - loss: 0.0024 - accuracy: 0.6075 - val_loss: 0.0036 - val_accuracy: 0.6000\n",
            "Epoch 145/500\n",
            "107/107 [==============================] - 0s 279us/sample - loss: 0.0021 - accuracy: 0.6449 - val_loss: 0.0043 - val_accuracy: 0.6500\n",
            "Epoch 146/500\n",
            "107/107 [==============================] - 0s 320us/sample - loss: 0.0024 - accuracy: 0.5888 - val_loss: 0.0050 - val_accuracy: 0.5500\n",
            "Epoch 147/500\n",
            "107/107 [==============================] - 0s 363us/sample - loss: 0.0023 - accuracy: 0.6355 - val_loss: 0.0036 - val_accuracy: 0.7500\n",
            "Epoch 148/500\n",
            "107/107 [==============================] - 0s 299us/sample - loss: 0.0022 - accuracy: 0.6729 - val_loss: 0.0047 - val_accuracy: 0.5000\n",
            "Epoch 149/500\n",
            "107/107 [==============================] - 0s 301us/sample - loss: 0.0022 - accuracy: 0.5701 - val_loss: 0.0034 - val_accuracy: 0.7500\n",
            "Epoch 150/500\n",
            "107/107 [==============================] - 0s 339us/sample - loss: 0.0023 - accuracy: 0.6542 - val_loss: 0.0042 - val_accuracy: 0.6500\n",
            "Epoch 151/500\n",
            "107/107 [==============================] - 0s 309us/sample - loss: 0.0022 - accuracy: 0.6168 - val_loss: 0.0038 - val_accuracy: 0.7000\n",
            "Epoch 152/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0021 - accuracy: 0.6729 - val_loss: 0.0041 - val_accuracy: 0.6000\n",
            "Epoch 153/500\n",
            "107/107 [==============================] - 0s 342us/sample - loss: 0.0021 - accuracy: 0.6542 - val_loss: 0.0035 - val_accuracy: 0.7000\n",
            "Epoch 154/500\n",
            "107/107 [==============================] - 0s 324us/sample - loss: 0.0020 - accuracy: 0.7196 - val_loss: 0.0043 - val_accuracy: 0.6500\n",
            "Epoch 155/500\n",
            "107/107 [==============================] - 0s 309us/sample - loss: 0.0022 - accuracy: 0.6355 - val_loss: 0.0038 - val_accuracy: 0.7000\n",
            "Epoch 156/500\n",
            "107/107 [==============================] - 0s 283us/sample - loss: 0.0023 - accuracy: 0.7009 - val_loss: 0.0041 - val_accuracy: 0.5500\n",
            "Epoch 157/500\n",
            "107/107 [==============================] - 0s 306us/sample - loss: 0.0021 - accuracy: 0.6449 - val_loss: 0.0040 - val_accuracy: 0.7000\n",
            "Epoch 158/500\n",
            "107/107 [==============================] - 0s 298us/sample - loss: 0.0022 - accuracy: 0.6449 - val_loss: 0.0047 - val_accuracy: 0.6000\n",
            "Epoch 159/500\n",
            "107/107 [==============================] - 0s 292us/sample - loss: 0.0022 - accuracy: 0.6168 - val_loss: 0.0038 - val_accuracy: 0.6500\n",
            "Epoch 160/500\n",
            "107/107 [==============================] - 0s 318us/sample - loss: 0.0023 - accuracy: 0.6168 - val_loss: 0.0047 - val_accuracy: 0.6000\n",
            "Epoch 161/500\n",
            "107/107 [==============================] - 0s 284us/sample - loss: 0.0023 - accuracy: 0.6355 - val_loss: 0.0038 - val_accuracy: 0.7500\n",
            "Epoch 162/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0026 - accuracy: 0.6075 - val_loss: 0.0053 - val_accuracy: 0.5500\n",
            "Epoch 163/500\n",
            "107/107 [==============================] - 0s 289us/sample - loss: 0.0024 - accuracy: 0.6075 - val_loss: 0.0037 - val_accuracy: 0.7500\n",
            "Epoch 164/500\n",
            "107/107 [==============================] - 0s 292us/sample - loss: 0.0025 - accuracy: 0.6075 - val_loss: 0.0056 - val_accuracy: 0.5000\n",
            "Epoch 165/500\n",
            "107/107 [==============================] - 0s 295us/sample - loss: 0.0023 - accuracy: 0.5981 - val_loss: 0.0036 - val_accuracy: 0.6000\n",
            "Epoch 166/500\n",
            "107/107 [==============================] - 0s 304us/sample - loss: 0.0023 - accuracy: 0.6262 - val_loss: 0.0046 - val_accuracy: 0.6500\n",
            "Epoch 167/500\n",
            "107/107 [==============================] - 0s 334us/sample - loss: 0.0024 - accuracy: 0.6168 - val_loss: 0.0039 - val_accuracy: 0.5500\n",
            "Epoch 168/500\n",
            "107/107 [==============================] - 0s 365us/sample - loss: 0.0024 - accuracy: 0.6822 - val_loss: 0.0040 - val_accuracy: 0.7500\n",
            "Epoch 169/500\n",
            "107/107 [==============================] - 0s 298us/sample - loss: 0.0024 - accuracy: 0.6075 - val_loss: 0.0039 - val_accuracy: 0.6000\n",
            "Epoch 170/500\n",
            "107/107 [==============================] - 0s 299us/sample - loss: 0.0024 - accuracy: 0.6355 - val_loss: 0.0051 - val_accuracy: 0.5500\n",
            "Epoch 171/500\n",
            "107/107 [==============================] - 0s 301us/sample - loss: 0.0025 - accuracy: 0.6075 - val_loss: 0.0051 - val_accuracy: 0.6000\n",
            "Epoch 172/500\n",
            "107/107 [==============================] - 0s 299us/sample - loss: 0.0026 - accuracy: 0.6262 - val_loss: 0.0052 - val_accuracy: 0.6000\n",
            "Epoch 173/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0030 - accuracy: 0.6262 - val_loss: 0.0052 - val_accuracy: 0.5500\n",
            "Epoch 174/500\n",
            "107/107 [==============================] - 0s 329us/sample - loss: 0.0025 - accuracy: 0.6168 - val_loss: 0.0044 - val_accuracy: 0.6500\n",
            "Epoch 175/500\n",
            "107/107 [==============================] - 0s 382us/sample - loss: 0.0023 - accuracy: 0.6168 - val_loss: 0.0052 - val_accuracy: 0.7000\n",
            "Epoch 176/500\n",
            "107/107 [==============================] - 0s 351us/sample - loss: 0.0027 - accuracy: 0.6075 - val_loss: 0.0055 - val_accuracy: 0.7000\n",
            "Epoch 177/500\n",
            "107/107 [==============================] - 0s 359us/sample - loss: 0.0024 - accuracy: 0.6542 - val_loss: 0.0039 - val_accuracy: 0.5500\n",
            "Epoch 178/500\n",
            "107/107 [==============================] - 0s 395us/sample - loss: 0.0025 - accuracy: 0.6168 - val_loss: 0.0053 - val_accuracy: 0.4500\n",
            "Epoch 179/500\n",
            "107/107 [==============================] - 0s 336us/sample - loss: 0.0026 - accuracy: 0.5981 - val_loss: 0.0038 - val_accuracy: 0.6000\n",
            "Epoch 180/500\n",
            "107/107 [==============================] - 0s 343us/sample - loss: 0.0025 - accuracy: 0.5888 - val_loss: 0.0036 - val_accuracy: 0.7000\n",
            "Epoch 181/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0024 - accuracy: 0.6355 - val_loss: 0.0060 - val_accuracy: 0.4500\n",
            "Epoch 182/500\n",
            "107/107 [==============================] - 0s 314us/sample - loss: 0.0027 - accuracy: 0.5888 - val_loss: 0.0033 - val_accuracy: 0.7500\n",
            "Epoch 183/500\n",
            "107/107 [==============================] - 0s 319us/sample - loss: 0.0023 - accuracy: 0.6636 - val_loss: 0.0042 - val_accuracy: 0.5500\n",
            "Epoch 184/500\n",
            "107/107 [==============================] - 0s 293us/sample - loss: 0.0023 - accuracy: 0.6729 - val_loss: 0.0047 - val_accuracy: 0.6500\n",
            "Epoch 185/500\n",
            "107/107 [==============================] - 0s 289us/sample - loss: 0.0026 - accuracy: 0.6262 - val_loss: 0.0035 - val_accuracy: 0.5500\n",
            "Epoch 186/500\n",
            "107/107 [==============================] - 0s 287us/sample - loss: 0.0023 - accuracy: 0.6729 - val_loss: 0.0038 - val_accuracy: 0.7000\n",
            "Epoch 187/500\n",
            "107/107 [==============================] - 0s 318us/sample - loss: 0.0025 - accuracy: 0.6168 - val_loss: 0.0037 - val_accuracy: 0.6000\n",
            "Epoch 188/500\n",
            "107/107 [==============================] - 0s 372us/sample - loss: 0.0022 - accuracy: 0.6542 - val_loss: 0.0038 - val_accuracy: 0.7500\n",
            "Epoch 189/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0022 - accuracy: 0.6355 - val_loss: 0.0038 - val_accuracy: 0.6500\n",
            "Epoch 190/500\n",
            "107/107 [==============================] - 0s 332us/sample - loss: 0.0021 - accuracy: 0.6355 - val_loss: 0.0037 - val_accuracy: 0.7500\n",
            "Epoch 191/500\n",
            "107/107 [==============================] - 0s 321us/sample - loss: 0.0020 - accuracy: 0.6729 - val_loss: 0.0043 - val_accuracy: 0.6000\n",
            "Epoch 192/500\n",
            "107/107 [==============================] - 0s 338us/sample - loss: 0.0021 - accuracy: 0.6355 - val_loss: 0.0039 - val_accuracy: 0.7000\n",
            "Epoch 193/500\n",
            "107/107 [==============================] - 0s 316us/sample - loss: 0.0021 - accuracy: 0.7009 - val_loss: 0.0050 - val_accuracy: 0.6000\n",
            "Epoch 194/500\n",
            "107/107 [==============================] - 0s 357us/sample - loss: 0.0021 - accuracy: 0.6355 - val_loss: 0.0040 - val_accuracy: 0.7000\n",
            "Epoch 195/500\n",
            "107/107 [==============================] - 0s 381us/sample - loss: 0.0021 - accuracy: 0.7009 - val_loss: 0.0051 - val_accuracy: 0.4500\n",
            "Epoch 196/500\n",
            "107/107 [==============================] - 0s 346us/sample - loss: 0.0022 - accuracy: 0.6449 - val_loss: 0.0036 - val_accuracy: 0.7500\n",
            "Epoch 197/500\n",
            "107/107 [==============================] - 0s 326us/sample - loss: 0.0022 - accuracy: 0.6636 - val_loss: 0.0047 - val_accuracy: 0.4500\n",
            "Epoch 198/500\n",
            "107/107 [==============================] - 0s 332us/sample - loss: 0.0024 - accuracy: 0.6822 - val_loss: 0.0039 - val_accuracy: 0.6500\n",
            "Epoch 199/500\n",
            "107/107 [==============================] - 0s 305us/sample - loss: 0.0023 - accuracy: 0.6449 - val_loss: 0.0045 - val_accuracy: 0.5500\n",
            "Epoch 200/500\n",
            "107/107 [==============================] - 0s 274us/sample - loss: 0.0020 - accuracy: 0.6542 - val_loss: 0.0043 - val_accuracy: 0.7000\n",
            "Epoch 201/500\n",
            "107/107 [==============================] - 0s 291us/sample - loss: 0.0023 - accuracy: 0.5888 - val_loss: 0.0050 - val_accuracy: 0.5000\n",
            "Epoch 202/500\n",
            "107/107 [==============================] - 0s 314us/sample - loss: 0.0021 - accuracy: 0.6262 - val_loss: 0.0043 - val_accuracy: 0.6500\n",
            "Epoch 203/500\n",
            "107/107 [==============================] - 0s 387us/sample - loss: 0.0020 - accuracy: 0.6822 - val_loss: 0.0047 - val_accuracy: 0.6500\n",
            "Epoch 204/500\n",
            "107/107 [==============================] - 0s 341us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0041 - val_accuracy: 0.7000\n",
            "Epoch 205/500\n",
            "107/107 [==============================] - 0s 287us/sample - loss: 0.0021 - accuracy: 0.6916 - val_loss: 0.0042 - val_accuracy: 0.5000\n",
            "Epoch 206/500\n",
            "107/107 [==============================] - 0s 276us/sample - loss: 0.0020 - accuracy: 0.6822 - val_loss: 0.0047 - val_accuracy: 0.6000\n",
            "Epoch 207/500\n",
            "107/107 [==============================] - 0s 296us/sample - loss: 0.0022 - accuracy: 0.6542 - val_loss: 0.0051 - val_accuracy: 0.5000\n",
            "Epoch 208/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0022 - accuracy: 0.6636 - val_loss: 0.0049 - val_accuracy: 0.6500\n",
            "Epoch 209/500\n",
            "107/107 [==============================] - 0s 330us/sample - loss: 0.0023 - accuracy: 0.6822 - val_loss: 0.0053 - val_accuracy: 0.4500\n",
            "Epoch 210/500\n",
            "107/107 [==============================] - 0s 285us/sample - loss: 0.0021 - accuracy: 0.6542 - val_loss: 0.0041 - val_accuracy: 0.7000\n",
            "Epoch 211/500\n",
            "107/107 [==============================] - 0s 314us/sample - loss: 0.0022 - accuracy: 0.6822 - val_loss: 0.0052 - val_accuracy: 0.5500\n",
            "Epoch 212/500\n",
            "107/107 [==============================] - 0s 301us/sample - loss: 0.0022 - accuracy: 0.6542 - val_loss: 0.0043 - val_accuracy: 0.6500\n",
            "Epoch 213/500\n",
            "107/107 [==============================] - 0s 294us/sample - loss: 0.0021 - accuracy: 0.6822 - val_loss: 0.0043 - val_accuracy: 0.5000\n",
            "Epoch 214/500\n",
            "107/107 [==============================] - 0s 292us/sample - loss: 0.0021 - accuracy: 0.6075 - val_loss: 0.0042 - val_accuracy: 0.7000\n",
            "Epoch 215/500\n",
            "107/107 [==============================] - 0s 326us/sample - loss: 0.0023 - accuracy: 0.5794 - val_loss: 0.0051 - val_accuracy: 0.6000\n",
            "Epoch 216/500\n",
            "107/107 [==============================] - 0s 356us/sample - loss: 0.0022 - accuracy: 0.6636 - val_loss: 0.0047 - val_accuracy: 0.6500\n",
            "Epoch 217/500\n",
            "107/107 [==============================] - 0s 333us/sample - loss: 0.0023 - accuracy: 0.6636 - val_loss: 0.0050 - val_accuracy: 0.5500\n",
            "Epoch 218/500\n",
            "107/107 [==============================] - 0s 333us/sample - loss: 0.0021 - accuracy: 0.6636 - val_loss: 0.0041 - val_accuracy: 0.6500\n",
            "Epoch 219/500\n",
            "107/107 [==============================] - 0s 312us/sample - loss: 0.0021 - accuracy: 0.6542 - val_loss: 0.0053 - val_accuracy: 0.6500\n",
            "Epoch 220/500\n",
            "107/107 [==============================] - 0s 287us/sample - loss: 0.0021 - accuracy: 0.6542 - val_loss: 0.0042 - val_accuracy: 0.6500\n",
            "Epoch 221/500\n",
            "107/107 [==============================] - 0s 282us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0044 - val_accuracy: 0.5500\n",
            "Epoch 222/500\n",
            "107/107 [==============================] - 0s 323us/sample - loss: 0.0020 - accuracy: 0.7103 - val_loss: 0.0041 - val_accuracy: 0.6000\n",
            "Epoch 223/500\n",
            "107/107 [==============================] - 0s 412us/sample - loss: 0.0021 - accuracy: 0.6822 - val_loss: 0.0050 - val_accuracy: 0.5000\n",
            "Epoch 224/500\n",
            "107/107 [==============================] - 0s 326us/sample - loss: 0.0022 - accuracy: 0.6822 - val_loss: 0.0045 - val_accuracy: 0.6500\n",
            "Epoch 225/500\n",
            "107/107 [==============================] - 0s 315us/sample - loss: 0.0022 - accuracy: 0.7009 - val_loss: 0.0044 - val_accuracy: 0.6500\n",
            "Epoch 226/500\n",
            "107/107 [==============================] - 0s 315us/sample - loss: 0.0018 - accuracy: 0.6729 - val_loss: 0.0046 - val_accuracy: 0.7000\n",
            "Epoch 227/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0020 - accuracy: 0.5888 - val_loss: 0.0045 - val_accuracy: 0.6500\n",
            "Epoch 228/500\n",
            "107/107 [==============================] - 0s 320us/sample - loss: 0.0019 - accuracy: 0.6729 - val_loss: 0.0049 - val_accuracy: 0.6500\n",
            "Epoch 229/500\n",
            "107/107 [==============================] - 0s 302us/sample - loss: 0.0021 - accuracy: 0.6542 - val_loss: 0.0047 - val_accuracy: 0.6500\n",
            "Epoch 230/500\n",
            "107/107 [==============================] - 0s 277us/sample - loss: 0.0019 - accuracy: 0.6822 - val_loss: 0.0047 - val_accuracy: 0.6500\n",
            "Epoch 231/500\n",
            "107/107 [==============================] - 0s 315us/sample - loss: 0.0021 - accuracy: 0.6542 - val_loss: 0.0052 - val_accuracy: 0.5000\n",
            "Epoch 232/500\n",
            "107/107 [==============================] - 0s 309us/sample - loss: 0.0022 - accuracy: 0.6729 - val_loss: 0.0046 - val_accuracy: 0.6000\n",
            "Epoch 233/500\n",
            "107/107 [==============================] - 0s 277us/sample - loss: 0.0023 - accuracy: 0.6636 - val_loss: 0.0046 - val_accuracy: 0.5000\n",
            "Epoch 234/500\n",
            "107/107 [==============================] - 0s 270us/sample - loss: 0.0021 - accuracy: 0.6168 - val_loss: 0.0055 - val_accuracy: 0.6000\n",
            "Epoch 235/500\n",
            "107/107 [==============================] - 0s 316us/sample - loss: 0.0024 - accuracy: 0.6168 - val_loss: 0.0055 - val_accuracy: 0.6500\n",
            "Epoch 236/500\n",
            "107/107 [==============================] - 0s 301us/sample - loss: 0.0022 - accuracy: 0.6449 - val_loss: 0.0051 - val_accuracy: 0.6500\n",
            "Epoch 237/500\n",
            "107/107 [==============================] - 0s 314us/sample - loss: 0.0024 - accuracy: 0.6355 - val_loss: 0.0060 - val_accuracy: 0.5000\n",
            "Epoch 238/500\n",
            "107/107 [==============================] - 0s 285us/sample - loss: 0.0022 - accuracy: 0.6355 - val_loss: 0.0050 - val_accuracy: 0.6500\n",
            "Epoch 239/500\n",
            "107/107 [==============================] - 0s 283us/sample - loss: 0.0021 - accuracy: 0.6262 - val_loss: 0.0054 - val_accuracy: 0.7500\n",
            "Epoch 240/500\n",
            "107/107 [==============================] - 0s 291us/sample - loss: 0.0023 - accuracy: 0.6075 - val_loss: 0.0054 - val_accuracy: 0.6500\n",
            "Epoch 241/500\n",
            "107/107 [==============================] - 0s 289us/sample - loss: 0.0021 - accuracy: 0.6916 - val_loss: 0.0047 - val_accuracy: 0.5500\n",
            "Epoch 242/500\n",
            "107/107 [==============================] - 0s 308us/sample - loss: 0.0023 - accuracy: 0.6075 - val_loss: 0.0043 - val_accuracy: 0.6500\n",
            "Epoch 243/500\n",
            "107/107 [==============================] - 0s 316us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0055 - val_accuracy: 0.7000\n",
            "Epoch 244/500\n",
            "107/107 [==============================] - 0s 276us/sample - loss: 0.0020 - accuracy: 0.6449 - val_loss: 0.0052 - val_accuracy: 0.5000\n",
            "Epoch 245/500\n",
            "107/107 [==============================] - 0s 293us/sample - loss: 0.0023 - accuracy: 0.6729 - val_loss: 0.0045 - val_accuracy: 0.7000\n",
            "Epoch 246/500\n",
            "107/107 [==============================] - 0s 276us/sample - loss: 0.0023 - accuracy: 0.6449 - val_loss: 0.0045 - val_accuracy: 0.5000\n",
            "Epoch 247/500\n",
            "107/107 [==============================] - 0s 305us/sample - loss: 0.0020 - accuracy: 0.6542 - val_loss: 0.0053 - val_accuracy: 0.7000\n",
            "Epoch 248/500\n",
            "107/107 [==============================] - 0s 323us/sample - loss: 0.0022 - accuracy: 0.6636 - val_loss: 0.0058 - val_accuracy: 0.6500\n",
            "Epoch 249/500\n",
            "107/107 [==============================] - 0s 304us/sample - loss: 0.0022 - accuracy: 0.6542 - val_loss: 0.0052 - val_accuracy: 0.6500\n",
            "Epoch 250/500\n",
            "107/107 [==============================] - 0s 320us/sample - loss: 0.0022 - accuracy: 0.6729 - val_loss: 0.0056 - val_accuracy: 0.5500\n",
            "Epoch 251/500\n",
            "107/107 [==============================] - 0s 321us/sample - loss: 0.0021 - accuracy: 0.6075 - val_loss: 0.0047 - val_accuracy: 0.6000\n",
            "Epoch 252/500\n",
            "107/107 [==============================] - 0s 449us/sample - loss: 0.0021 - accuracy: 0.6729 - val_loss: 0.0064 - val_accuracy: 0.6500\n",
            "Epoch 253/500\n",
            "107/107 [==============================] - 0s 345us/sample - loss: 0.0024 - accuracy: 0.6168 - val_loss: 0.0053 - val_accuracy: 0.7000\n",
            "Epoch 254/500\n",
            "107/107 [==============================] - 0s 400us/sample - loss: 0.0020 - accuracy: 0.7103 - val_loss: 0.0053 - val_accuracy: 0.5000\n",
            "Epoch 255/500\n",
            "107/107 [==============================] - 0s 339us/sample - loss: 0.0020 - accuracy: 0.6542 - val_loss: 0.0047 - val_accuracy: 0.6500\n",
            "Epoch 256/500\n",
            "107/107 [==============================] - 0s 338us/sample - loss: 0.0019 - accuracy: 0.7009 - val_loss: 0.0054 - val_accuracy: 0.6500\n",
            "Epoch 257/500\n",
            "107/107 [==============================] - 0s 315us/sample - loss: 0.0021 - accuracy: 0.6262 - val_loss: 0.0051 - val_accuracy: 0.7000\n",
            "Epoch 258/500\n",
            "107/107 [==============================] - 0s 371us/sample - loss: 0.0021 - accuracy: 0.6822 - val_loss: 0.0051 - val_accuracy: 0.5500\n",
            "Epoch 259/500\n",
            "107/107 [==============================] - 0s 391us/sample - loss: 0.0021 - accuracy: 0.6449 - val_loss: 0.0046 - val_accuracy: 0.6500\n",
            "Epoch 260/500\n",
            "107/107 [==============================] - 0s 341us/sample - loss: 0.0022 - accuracy: 0.6636 - val_loss: 0.0060 - val_accuracy: 0.5500\n",
            "Epoch 261/500\n",
            "107/107 [==============================] - 0s 347us/sample - loss: 0.0022 - accuracy: 0.6355 - val_loss: 0.0050 - val_accuracy: 0.6500\n",
            "Epoch 262/500\n",
            "107/107 [==============================] - 0s 351us/sample - loss: 0.0022 - accuracy: 0.6636 - val_loss: 0.0077 - val_accuracy: 0.4500\n",
            "Epoch 263/500\n",
            "107/107 [==============================] - 0s 350us/sample - loss: 0.0024 - accuracy: 0.6449 - val_loss: 0.0043 - val_accuracy: 0.6500\n",
            "Epoch 264/500\n",
            "107/107 [==============================] - 0s 367us/sample - loss: 0.0023 - accuracy: 0.6542 - val_loss: 0.0062 - val_accuracy: 0.5000\n",
            "Epoch 265/500\n",
            "107/107 [==============================] - 0s 372us/sample - loss: 0.0023 - accuracy: 0.6542 - val_loss: 0.0053 - val_accuracy: 0.6000\n",
            "Epoch 266/500\n",
            "107/107 [==============================] - 0s 383us/sample - loss: 0.0023 - accuracy: 0.6822 - val_loss: 0.0051 - val_accuracy: 0.5500\n",
            "Epoch 267/500\n",
            "107/107 [==============================] - 0s 351us/sample - loss: 0.0021 - accuracy: 0.6729 - val_loss: 0.0054 - val_accuracy: 0.6000\n",
            "Epoch 268/500\n",
            "107/107 [==============================] - 0s 338us/sample - loss: 0.0021 - accuracy: 0.6636 - val_loss: 0.0052 - val_accuracy: 0.7000\n",
            "Epoch 269/500\n",
            "107/107 [==============================] - 0s 364us/sample - loss: 0.0019 - accuracy: 0.6916 - val_loss: 0.0055 - val_accuracy: 0.6000\n",
            "Epoch 270/500\n",
            "107/107 [==============================] - 0s 458us/sample - loss: 0.0020 - accuracy: 0.6822 - val_loss: 0.0045 - val_accuracy: 0.6500\n",
            "Epoch 271/500\n",
            "107/107 [==============================] - 0s 359us/sample - loss: 0.0019 - accuracy: 0.7103 - val_loss: 0.0054 - val_accuracy: 0.6000\n",
            "Epoch 272/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0019 - accuracy: 0.7103 - val_loss: 0.0051 - val_accuracy: 0.5500\n",
            "Epoch 273/500\n",
            "107/107 [==============================] - 0s 365us/sample - loss: 0.0020 - accuracy: 0.7290 - val_loss: 0.0053 - val_accuracy: 0.6000\n",
            "Epoch 274/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0021 - accuracy: 0.7009 - val_loss: 0.0050 - val_accuracy: 0.5500\n",
            "Epoch 275/500\n",
            "107/107 [==============================] - 0s 360us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0053 - val_accuracy: 0.6000\n",
            "Epoch 276/500\n",
            "107/107 [==============================] - 0s 460us/sample - loss: 0.0020 - accuracy: 0.7009 - val_loss: 0.0049 - val_accuracy: 0.6500\n",
            "Epoch 277/500\n",
            "107/107 [==============================] - 0s 394us/sample - loss: 0.0018 - accuracy: 0.7103 - val_loss: 0.0052 - val_accuracy: 0.6500\n",
            "Epoch 278/500\n",
            "107/107 [==============================] - 0s 397us/sample - loss: 0.0019 - accuracy: 0.7290 - val_loss: 0.0050 - val_accuracy: 0.6500\n",
            "Epoch 279/500\n",
            "107/107 [==============================] - 0s 350us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0049 - val_accuracy: 0.7000\n",
            "Epoch 280/500\n",
            "107/107 [==============================] - 0s 331us/sample - loss: 0.0020 - accuracy: 0.7009 - val_loss: 0.0053 - val_accuracy: 0.4500\n",
            "Epoch 281/500\n",
            "107/107 [==============================] - 0s 360us/sample - loss: 0.0021 - accuracy: 0.6636 - val_loss: 0.0051 - val_accuracy: 0.6000\n",
            "Epoch 282/500\n",
            "107/107 [==============================] - 0s 358us/sample - loss: 0.0022 - accuracy: 0.6542 - val_loss: 0.0056 - val_accuracy: 0.5500\n",
            "Epoch 283/500\n",
            "107/107 [==============================] - 0s 421us/sample - loss: 0.0020 - accuracy: 0.6729 - val_loss: 0.0053 - val_accuracy: 0.6500\n",
            "Epoch 284/500\n",
            "107/107 [==============================] - 0s 353us/sample - loss: 0.0022 - accuracy: 0.6729 - val_loss: 0.0064 - val_accuracy: 0.6000\n",
            "Epoch 285/500\n",
            "107/107 [==============================] - 0s 366us/sample - loss: 0.0020 - accuracy: 0.6822 - val_loss: 0.0047 - val_accuracy: 0.6000\n",
            "Epoch 286/500\n",
            "107/107 [==============================] - 0s 375us/sample - loss: 0.0020 - accuracy: 0.7009 - val_loss: 0.0063 - val_accuracy: 0.7000\n",
            "Epoch 287/500\n",
            "107/107 [==============================] - 0s 355us/sample - loss: 0.0024 - accuracy: 0.6449 - val_loss: 0.0052 - val_accuracy: 0.6000\n",
            "Epoch 288/500\n",
            "107/107 [==============================] - 0s 349us/sample - loss: 0.0018 - accuracy: 0.6822 - val_loss: 0.0054 - val_accuracy: 0.5500\n",
            "Epoch 289/500\n",
            "107/107 [==============================] - 0s 357us/sample - loss: 0.0021 - accuracy: 0.7009 - val_loss: 0.0047 - val_accuracy: 0.6000\n",
            "Epoch 290/500\n",
            "107/107 [==============================] - 0s 342us/sample - loss: 0.0019 - accuracy: 0.6636 - val_loss: 0.0052 - val_accuracy: 0.6000\n",
            "Epoch 291/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0023 - accuracy: 0.6822 - val_loss: 0.0052 - val_accuracy: 0.6000\n",
            "Epoch 292/500\n",
            "107/107 [==============================] - 0s 366us/sample - loss: 0.0024 - accuracy: 0.6729 - val_loss: 0.0055 - val_accuracy: 0.5500\n",
            "Epoch 293/500\n",
            "107/107 [==============================] - 0s 385us/sample - loss: 0.0025 - accuracy: 0.6355 - val_loss: 0.0051 - val_accuracy: 0.5500\n",
            "Epoch 294/500\n",
            "107/107 [==============================] - 0s 347us/sample - loss: 0.0023 - accuracy: 0.6262 - val_loss: 0.0059 - val_accuracy: 0.4500\n",
            "Epoch 295/500\n",
            "107/107 [==============================] - 0s 336us/sample - loss: 0.0022 - accuracy: 0.6355 - val_loss: 0.0057 - val_accuracy: 0.5500\n",
            "Epoch 296/500\n",
            "107/107 [==============================] - 0s 416us/sample - loss: 0.0024 - accuracy: 0.6636 - val_loss: 0.0052 - val_accuracy: 0.6000\n",
            "Epoch 297/500\n",
            "107/107 [==============================] - 0s 361us/sample - loss: 0.0021 - accuracy: 0.7009 - val_loss: 0.0051 - val_accuracy: 0.5500\n",
            "Epoch 298/500\n",
            "107/107 [==============================] - 0s 397us/sample - loss: 0.0021 - accuracy: 0.6916 - val_loss: 0.0052 - val_accuracy: 0.4500\n",
            "Epoch 299/500\n",
            "107/107 [==============================] - 0s 371us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0050 - val_accuracy: 0.7000\n",
            "Epoch 300/500\n",
            "107/107 [==============================] - 0s 420us/sample - loss: 0.0020 - accuracy: 0.6449 - val_loss: 0.0052 - val_accuracy: 0.6000\n",
            "Epoch 301/500\n",
            "107/107 [==============================] - 0s 479us/sample - loss: 0.0019 - accuracy: 0.7383 - val_loss: 0.0061 - val_accuracy: 0.5500\n",
            "Epoch 302/500\n",
            "107/107 [==============================] - 0s 361us/sample - loss: 0.0023 - accuracy: 0.6449 - val_loss: 0.0052 - val_accuracy: 0.5000\n",
            "Epoch 303/500\n",
            "107/107 [==============================] - 0s 343us/sample - loss: 0.0021 - accuracy: 0.7196 - val_loss: 0.0050 - val_accuracy: 0.6500\n",
            "Epoch 304/500\n",
            "107/107 [==============================] - 0s 338us/sample - loss: 0.0020 - accuracy: 0.7103 - val_loss: 0.0055 - val_accuracy: 0.6500\n",
            "Epoch 305/500\n",
            "107/107 [==============================] - 0s 389us/sample - loss: 0.0018 - accuracy: 0.7009 - val_loss: 0.0051 - val_accuracy: 0.6500\n",
            "Epoch 306/500\n",
            "107/107 [==============================] - 0s 369us/sample - loss: 0.0019 - accuracy: 0.6916 - val_loss: 0.0052 - val_accuracy: 0.6500\n",
            "Epoch 307/500\n",
            "107/107 [==============================] - 0s 357us/sample - loss: 0.0018 - accuracy: 0.7196 - val_loss: 0.0053 - val_accuracy: 0.6000\n",
            "Epoch 308/500\n",
            "107/107 [==============================] - 0s 422us/sample - loss: 0.0017 - accuracy: 0.7196 - val_loss: 0.0050 - val_accuracy: 0.6500\n",
            "Epoch 309/500\n",
            "107/107 [==============================] - 0s 408us/sample - loss: 0.0018 - accuracy: 0.7103 - val_loss: 0.0046 - val_accuracy: 0.6000\n",
            "Epoch 310/500\n",
            "107/107 [==============================] - 0s 345us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0055 - val_accuracy: 0.6500\n",
            "Epoch 311/500\n",
            "107/107 [==============================] - 0s 330us/sample - loss: 0.0018 - accuracy: 0.7290 - val_loss: 0.0052 - val_accuracy: 0.6000\n",
            "Epoch 312/500\n",
            "107/107 [==============================] - 0s 387us/sample - loss: 0.0018 - accuracy: 0.7383 - val_loss: 0.0057 - val_accuracy: 0.5500\n",
            "Epoch 313/500\n",
            "107/107 [==============================] - 0s 368us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0056 - val_accuracy: 0.6000\n",
            "Epoch 314/500\n",
            "107/107 [==============================] - 0s 387us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0055 - val_accuracy: 0.6000\n",
            "Epoch 315/500\n",
            "107/107 [==============================] - 0s 307us/sample - loss: 0.0018 - accuracy: 0.7196 - val_loss: 0.0050 - val_accuracy: 0.6000\n",
            "Epoch 316/500\n",
            "107/107 [==============================] - 0s 368us/sample - loss: 0.0018 - accuracy: 0.7477 - val_loss: 0.0054 - val_accuracy: 0.6500\n",
            "Epoch 317/500\n",
            "107/107 [==============================] - 0s 395us/sample - loss: 0.0018 - accuracy: 0.7196 - val_loss: 0.0056 - val_accuracy: 0.6000\n",
            "Epoch 318/500\n",
            "107/107 [==============================] - 0s 374us/sample - loss: 0.0018 - accuracy: 0.7290 - val_loss: 0.0060 - val_accuracy: 0.6500\n",
            "Epoch 319/500\n",
            "107/107 [==============================] - 0s 345us/sample - loss: 0.0017 - accuracy: 0.7196 - val_loss: 0.0057 - val_accuracy: 0.6000\n",
            "Epoch 320/500\n",
            "107/107 [==============================] - 0s 339us/sample - loss: 0.0017 - accuracy: 0.7290 - val_loss: 0.0056 - val_accuracy: 0.6500\n",
            "Epoch 321/500\n",
            "107/107 [==============================] - 0s 331us/sample - loss: 0.0016 - accuracy: 0.7477 - val_loss: 0.0057 - val_accuracy: 0.6000\n",
            "Epoch 322/500\n",
            "107/107 [==============================] - 0s 371us/sample - loss: 0.0018 - accuracy: 0.7477 - val_loss: 0.0056 - val_accuracy: 0.6500\n",
            "Epoch 323/500\n",
            "107/107 [==============================] - 0s 402us/sample - loss: 0.0017 - accuracy: 0.7477 - val_loss: 0.0059 - val_accuracy: 0.6000\n",
            "Epoch 324/500\n",
            "107/107 [==============================] - 0s 362us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0054 - val_accuracy: 0.5500\n",
            "Epoch 325/500\n",
            "107/107 [==============================] - 0s 393us/sample - loss: 0.0017 - accuracy: 0.7196 - val_loss: 0.0063 - val_accuracy: 0.6000\n",
            "Epoch 326/500\n",
            "107/107 [==============================] - 0s 475us/sample - loss: 0.0020 - accuracy: 0.6822 - val_loss: 0.0052 - val_accuracy: 0.6500\n",
            "Epoch 327/500\n",
            "107/107 [==============================] - 0s 377us/sample - loss: 0.0017 - accuracy: 0.7103 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 328/500\n",
            "107/107 [==============================] - 0s 353us/sample - loss: 0.0019 - accuracy: 0.7103 - val_loss: 0.0054 - val_accuracy: 0.6500\n",
            "Epoch 329/500\n",
            "107/107 [==============================] - 0s 377us/sample - loss: 0.0017 - accuracy: 0.7290 - val_loss: 0.0058 - val_accuracy: 0.6000\n",
            "Epoch 330/500\n",
            "107/107 [==============================] - 0s 404us/sample - loss: 0.0018 - accuracy: 0.7570 - val_loss: 0.0059 - val_accuracy: 0.6500\n",
            "Epoch 331/500\n",
            "107/107 [==============================] - 0s 364us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0057 - val_accuracy: 0.6000\n",
            "Epoch 332/500\n",
            "107/107 [==============================] - 0s 362us/sample - loss: 0.0018 - accuracy: 0.7383 - val_loss: 0.0051 - val_accuracy: 0.6500\n",
            "Epoch 333/500\n",
            "107/107 [==============================] - 0s 355us/sample - loss: 0.0016 - accuracy: 0.7757 - val_loss: 0.0063 - val_accuracy: 0.6000\n",
            "Epoch 334/500\n",
            "107/107 [==============================] - 0s 356us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0052 - val_accuracy: 0.6000\n",
            "Epoch 335/500\n",
            "107/107 [==============================] - 0s 356us/sample - loss: 0.0018 - accuracy: 0.7290 - val_loss: 0.0068 - val_accuracy: 0.6000\n",
            "Epoch 336/500\n",
            "107/107 [==============================] - 0s 361us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0051 - val_accuracy: 0.6000\n",
            "Epoch 337/500\n",
            "107/107 [==============================] - 0s 397us/sample - loss: 0.0019 - accuracy: 0.7290 - val_loss: 0.0064 - val_accuracy: 0.6000\n",
            "Epoch 338/500\n",
            "107/107 [==============================] - 0s 349us/sample - loss: 0.0020 - accuracy: 0.6822 - val_loss: 0.0056 - val_accuracy: 0.6000\n",
            "Epoch 339/500\n",
            "107/107 [==============================] - 0s 362us/sample - loss: 0.0018 - accuracy: 0.7383 - val_loss: 0.0053 - val_accuracy: 0.6000\n",
            "Epoch 340/500\n",
            "107/107 [==============================] - 0s 361us/sample - loss: 0.0018 - accuracy: 0.7196 - val_loss: 0.0059 - val_accuracy: 0.6500\n",
            "Epoch 341/500\n",
            "107/107 [==============================] - 0s 338us/sample - loss: 0.0017 - accuracy: 0.7196 - val_loss: 0.0055 - val_accuracy: 0.6500\n",
            "Epoch 342/500\n",
            "107/107 [==============================] - 0s 334us/sample - loss: 0.0017 - accuracy: 0.7664 - val_loss: 0.0056 - val_accuracy: 0.6500\n",
            "Epoch 343/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0016 - accuracy: 0.7103 - val_loss: 0.0061 - val_accuracy: 0.6000\n",
            "Epoch 344/500\n",
            "107/107 [==============================] - 0s 386us/sample - loss: 0.0017 - accuracy: 0.7290 - val_loss: 0.0051 - val_accuracy: 0.6000\n",
            "Epoch 345/500\n",
            "107/107 [==============================] - 0s 380us/sample - loss: 0.0017 - accuracy: 0.7290 - val_loss: 0.0058 - val_accuracy: 0.6000\n",
            "Epoch 346/500\n",
            "107/107 [==============================] - 0s 362us/sample - loss: 0.0019 - accuracy: 0.6822 - val_loss: 0.0058 - val_accuracy: 0.5500\n",
            "Epoch 347/500\n",
            "107/107 [==============================] - 0s 363us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0065 - val_accuracy: 0.6500\n",
            "Epoch 348/500\n",
            "107/107 [==============================] - 0s 356us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0067 - val_accuracy: 0.5500\n",
            "Epoch 349/500\n",
            "107/107 [==============================] - 0s 459us/sample - loss: 0.0018 - accuracy: 0.6729 - val_loss: 0.0046 - val_accuracy: 0.7000\n",
            "Epoch 350/500\n",
            "107/107 [==============================] - 0s 473us/sample - loss: 0.0018 - accuracy: 0.6916 - val_loss: 0.0059 - val_accuracy: 0.6000\n",
            "Epoch 351/500\n",
            "107/107 [==============================] - 0s 380us/sample - loss: 0.0017 - accuracy: 0.6636 - val_loss: 0.0055 - val_accuracy: 0.6000\n",
            "Epoch 352/500\n",
            "107/107 [==============================] - 0s 375us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0066 - val_accuracy: 0.6000\n",
            "Epoch 353/500\n",
            "107/107 [==============================] - 0s 368us/sample - loss: 0.0019 - accuracy: 0.6636 - val_loss: 0.0065 - val_accuracy: 0.5500\n",
            "Epoch 354/500\n",
            "107/107 [==============================] - 0s 352us/sample - loss: 0.0022 - accuracy: 0.7196 - val_loss: 0.0058 - val_accuracy: 0.5500\n",
            "Epoch 355/500\n",
            "107/107 [==============================] - 0s 379us/sample - loss: 0.0018 - accuracy: 0.6916 - val_loss: 0.0045 - val_accuracy: 0.6500\n",
            "Epoch 356/500\n",
            "107/107 [==============================] - 0s 363us/sample - loss: 0.0020 - accuracy: 0.6542 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 357/500\n",
            "107/107 [==============================] - 0s 454us/sample - loss: 0.0024 - accuracy: 0.7196 - val_loss: 0.0058 - val_accuracy: 0.5500\n",
            "Epoch 358/500\n",
            "107/107 [==============================] - 0s 355us/sample - loss: 0.0024 - accuracy: 0.6262 - val_loss: 0.0055 - val_accuracy: 0.6500\n",
            "Epoch 359/500\n",
            "107/107 [==============================] - 0s 371us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0056 - val_accuracy: 0.7000\n",
            "Epoch 360/500\n",
            "107/107 [==============================] - 0s 410us/sample - loss: 0.0020 - accuracy: 0.7009 - val_loss: 0.0055 - val_accuracy: 0.6000\n",
            "Epoch 361/500\n",
            "107/107 [==============================] - 0s 359us/sample - loss: 0.0020 - accuracy: 0.7103 - val_loss: 0.0051 - val_accuracy: 0.6000\n",
            "Epoch 362/500\n",
            "107/107 [==============================] - 0s 354us/sample - loss: 0.0018 - accuracy: 0.6916 - val_loss: 0.0051 - val_accuracy: 0.6000\n",
            "Epoch 363/500\n",
            "107/107 [==============================] - 0s 353us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0070 - val_accuracy: 0.5500\n",
            "Epoch 364/500\n",
            "107/107 [==============================] - 0s 343us/sample - loss: 0.0021 - accuracy: 0.6542 - val_loss: 0.0051 - val_accuracy: 0.6500\n",
            "Epoch 365/500\n",
            "107/107 [==============================] - 0s 390us/sample - loss: 0.0020 - accuracy: 0.7290 - val_loss: 0.0070 - val_accuracy: 0.5500\n",
            "Epoch 366/500\n",
            "107/107 [==============================] - 0s 383us/sample - loss: 0.0024 - accuracy: 0.6542 - val_loss: 0.0056 - val_accuracy: 0.6000\n",
            "Epoch 367/500\n",
            "107/107 [==============================] - 0s 353us/sample - loss: 0.0020 - accuracy: 0.6729 - val_loss: 0.0066 - val_accuracy: 0.6000\n",
            "Epoch 368/500\n",
            "107/107 [==============================] - 0s 348us/sample - loss: 0.0025 - accuracy: 0.6542 - val_loss: 0.0075 - val_accuracy: 0.5500\n",
            "Epoch 369/500\n",
            "107/107 [==============================] - 0s 391us/sample - loss: 0.0019 - accuracy: 0.7009 - val_loss: 0.0054 - val_accuracy: 0.7000\n",
            "Epoch 370/500\n",
            "107/107 [==============================] - 0s 380us/sample - loss: 0.0018 - accuracy: 0.7290 - val_loss: 0.0063 - val_accuracy: 0.6000\n",
            "Epoch 371/500\n",
            "107/107 [==============================] - 0s 340us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0052 - val_accuracy: 0.5500\n",
            "Epoch 372/500\n",
            "107/107 [==============================] - 0s 403us/sample - loss: 0.0017 - accuracy: 0.7477 - val_loss: 0.0066 - val_accuracy: 0.6500\n",
            "Epoch 373/500\n",
            "107/107 [==============================] - 0s 350us/sample - loss: 0.0025 - accuracy: 0.6636 - val_loss: 0.0044 - val_accuracy: 0.7000\n",
            "Epoch 374/500\n",
            "107/107 [==============================] - 0s 337us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0068 - val_accuracy: 0.5500\n",
            "Epoch 375/500\n",
            "107/107 [==============================] - 0s 502us/sample - loss: 0.0019 - accuracy: 0.6729 - val_loss: 0.0054 - val_accuracy: 0.6500\n",
            "Epoch 376/500\n",
            "107/107 [==============================] - 0s 413us/sample - loss: 0.0020 - accuracy: 0.7290 - val_loss: 0.0063 - val_accuracy: 0.5500\n",
            "Epoch 377/500\n",
            "107/107 [==============================] - 0s 379us/sample - loss: 0.0017 - accuracy: 0.7290 - val_loss: 0.0056 - val_accuracy: 0.6500\n",
            "Epoch 378/500\n",
            "107/107 [==============================] - 0s 348us/sample - loss: 0.0019 - accuracy: 0.7009 - val_loss: 0.0065 - val_accuracy: 0.5500\n",
            "Epoch 379/500\n",
            "107/107 [==============================] - 0s 355us/sample - loss: 0.0020 - accuracy: 0.6636 - val_loss: 0.0048 - val_accuracy: 0.6500\n",
            "Epoch 380/500\n",
            "107/107 [==============================] - 0s 348us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0066 - val_accuracy: 0.6000\n",
            "Epoch 381/500\n",
            "107/107 [==============================] - 0s 371us/sample - loss: 0.0019 - accuracy: 0.6822 - val_loss: 0.0050 - val_accuracy: 0.6500\n",
            "Epoch 382/500\n",
            "107/107 [==============================] - 0s 357us/sample - loss: 0.0018 - accuracy: 0.7383 - val_loss: 0.0063 - val_accuracy: 0.5500\n",
            "Epoch 383/500\n",
            "107/107 [==============================] - 0s 385us/sample - loss: 0.0017 - accuracy: 0.7196 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 384/500\n",
            "107/107 [==============================] - 0s 370us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0065 - val_accuracy: 0.6000\n",
            "Epoch 385/500\n",
            "107/107 [==============================] - 0s 383us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0058 - val_accuracy: 0.7000\n",
            "Epoch 386/500\n",
            "107/107 [==============================] - 0s 375us/sample - loss: 0.0019 - accuracy: 0.6916 - val_loss: 0.0064 - val_accuracy: 0.6000\n",
            "Epoch 387/500\n",
            "107/107 [==============================] - 0s 363us/sample - loss: 0.0019 - accuracy: 0.7103 - val_loss: 0.0067 - val_accuracy: 0.6000\n",
            "Epoch 388/500\n",
            "107/107 [==============================] - 0s 352us/sample - loss: 0.0021 - accuracy: 0.7103 - val_loss: 0.0059 - val_accuracy: 0.6000\n",
            "Epoch 389/500\n",
            "107/107 [==============================] - 0s 365us/sample - loss: 0.0021 - accuracy: 0.7290 - val_loss: 0.0081 - val_accuracy: 0.5000\n",
            "Epoch 390/500\n",
            "107/107 [==============================] - 0s 339us/sample - loss: 0.0021 - accuracy: 0.6729 - val_loss: 0.0055 - val_accuracy: 0.5500\n",
            "Epoch 391/500\n",
            "107/107 [==============================] - 0s 373us/sample - loss: 0.0022 - accuracy: 0.6729 - val_loss: 0.0065 - val_accuracy: 0.6500\n",
            "Epoch 392/500\n",
            "107/107 [==============================] - 0s 374us/sample - loss: 0.0026 - accuracy: 0.6542 - val_loss: 0.0072 - val_accuracy: 0.5500\n",
            "Epoch 393/500\n",
            "107/107 [==============================] - 0s 373us/sample - loss: 0.0020 - accuracy: 0.6916 - val_loss: 0.0064 - val_accuracy: 0.5500\n",
            "Epoch 394/500\n",
            "107/107 [==============================] - 0s 376us/sample - loss: 0.0022 - accuracy: 0.6729 - val_loss: 0.0085 - val_accuracy: 0.5000\n",
            "Epoch 395/500\n",
            "107/107 [==============================] - 0s 386us/sample - loss: 0.0021 - accuracy: 0.6916 - val_loss: 0.0055 - val_accuracy: 0.6500\n",
            "Epoch 396/500\n",
            "107/107 [==============================] - 0s 383us/sample - loss: 0.0021 - accuracy: 0.7383 - val_loss: 0.0066 - val_accuracy: 0.6000\n",
            "Epoch 397/500\n",
            "107/107 [==============================] - 0s 410us/sample - loss: 0.0020 - accuracy: 0.7290 - val_loss: 0.0065 - val_accuracy: 0.6000\n",
            "Epoch 398/500\n",
            "107/107 [==============================] - 0s 357us/sample - loss: 0.0022 - accuracy: 0.7103 - val_loss: 0.0055 - val_accuracy: 0.5000\n",
            "Epoch 399/500\n",
            "107/107 [==============================] - 0s 495us/sample - loss: 0.0019 - accuracy: 0.7290 - val_loss: 0.0062 - val_accuracy: 0.6000\n",
            "Epoch 400/500\n",
            "107/107 [==============================] - 0s 382us/sample - loss: 0.0018 - accuracy: 0.6916 - val_loss: 0.0057 - val_accuracy: 0.5500\n",
            "Epoch 401/500\n",
            "107/107 [==============================] - 0s 369us/sample - loss: 0.0018 - accuracy: 0.7196 - val_loss: 0.0065 - val_accuracy: 0.6500\n",
            "Epoch 402/500\n",
            "107/107 [==============================] - 0s 379us/sample - loss: 0.0019 - accuracy: 0.6916 - val_loss: 0.0059 - val_accuracy: 0.5000\n",
            "Epoch 403/500\n",
            "107/107 [==============================] - 0s 361us/sample - loss: 0.0020 - accuracy: 0.7196 - val_loss: 0.0056 - val_accuracy: 0.6000\n",
            "Epoch 404/500\n",
            "107/107 [==============================] - 0s 360us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0063 - val_accuracy: 0.5500\n",
            "Epoch 405/500\n",
            "107/107 [==============================] - 0s 381us/sample - loss: 0.0019 - accuracy: 0.6916 - val_loss: 0.0070 - val_accuracy: 0.5500\n",
            "Epoch 406/500\n",
            "107/107 [==============================] - 0s 407us/sample - loss: 0.0021 - accuracy: 0.6729 - val_loss: 0.0074 - val_accuracy: 0.5500\n",
            "Epoch 407/500\n",
            "107/107 [==============================] - 0s 346us/sample - loss: 0.0021 - accuracy: 0.6822 - val_loss: 0.0055 - val_accuracy: 0.6000\n",
            "Epoch 408/500\n",
            "107/107 [==============================] - 0s 338us/sample - loss: 0.0020 - accuracy: 0.7103 - val_loss: 0.0060 - val_accuracy: 0.5500\n",
            "Epoch 409/500\n",
            "107/107 [==============================] - 0s 347us/sample - loss: 0.0021 - accuracy: 0.7009 - val_loss: 0.0058 - val_accuracy: 0.5500\n",
            "Epoch 410/500\n",
            "107/107 [==============================] - 0s 365us/sample - loss: 0.0018 - accuracy: 0.7290 - val_loss: 0.0071 - val_accuracy: 0.5500\n",
            "Epoch 411/500\n",
            "107/107 [==============================] - 0s 327us/sample - loss: 0.0019 - accuracy: 0.7383 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 412/500\n",
            "107/107 [==============================] - 0s 398us/sample - loss: 0.0020 - accuracy: 0.7009 - val_loss: 0.0057 - val_accuracy: 0.6500\n",
            "Epoch 413/500\n",
            "107/107 [==============================] - 0s 369us/sample - loss: 0.0017 - accuracy: 0.7664 - val_loss: 0.0067 - val_accuracy: 0.6500\n",
            "Epoch 414/500\n",
            "107/107 [==============================] - 0s 342us/sample - loss: 0.0020 - accuracy: 0.7009 - val_loss: 0.0059 - val_accuracy: 0.6000\n",
            "Epoch 415/500\n",
            "107/107 [==============================] - 0s 375us/sample - loss: 0.0019 - accuracy: 0.7196 - val_loss: 0.0064 - val_accuracy: 0.6000\n",
            "Epoch 416/500\n",
            "107/107 [==============================] - 0s 402us/sample - loss: 0.0019 - accuracy: 0.7383 - val_loss: 0.0065 - val_accuracy: 0.6000\n",
            "Epoch 417/500\n",
            "107/107 [==============================] - 0s 401us/sample - loss: 0.0017 - accuracy: 0.7196 - val_loss: 0.0057 - val_accuracy: 0.6000\n",
            "Epoch 418/500\n",
            "107/107 [==============================] - 0s 368us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0063 - val_accuracy: 0.6500\n",
            "Epoch 419/500\n",
            "107/107 [==============================] - 0s 378us/sample - loss: 0.0016 - accuracy: 0.7850 - val_loss: 0.0058 - val_accuracy: 0.6000\n",
            "Epoch 420/500\n",
            "107/107 [==============================] - 0s 378us/sample - loss: 0.0018 - accuracy: 0.7290 - val_loss: 0.0055 - val_accuracy: 0.6500\n",
            "Epoch 421/500\n",
            "107/107 [==============================] - 0s 410us/sample - loss: 0.0016 - accuracy: 0.7383 - val_loss: 0.0063 - val_accuracy: 0.6000\n",
            "Epoch 422/500\n",
            "107/107 [==============================] - 0s 352us/sample - loss: 0.0018 - accuracy: 0.7196 - val_loss: 0.0059 - val_accuracy: 0.6500\n",
            "Epoch 423/500\n",
            "107/107 [==============================] - 0s 446us/sample - loss: 0.0016 - accuracy: 0.7570 - val_loss: 0.0062 - val_accuracy: 0.6000\n",
            "Epoch 424/500\n",
            "107/107 [==============================] - 0s 358us/sample - loss: 0.0017 - accuracy: 0.7290 - val_loss: 0.0058 - val_accuracy: 0.6500\n",
            "Epoch 425/500\n",
            "107/107 [==============================] - 0s 339us/sample - loss: 0.0016 - accuracy: 0.7664 - val_loss: 0.0057 - val_accuracy: 0.6000\n",
            "Epoch 426/500\n",
            "107/107 [==============================] - 0s 346us/sample - loss: 0.0017 - accuracy: 0.7477 - val_loss: 0.0057 - val_accuracy: 0.6000\n",
            "Epoch 427/500\n",
            "107/107 [==============================] - 0s 360us/sample - loss: 0.0016 - accuracy: 0.7290 - val_loss: 0.0062 - val_accuracy: 0.6000\n",
            "Epoch 428/500\n",
            "107/107 [==============================] - 0s 341us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0058 - val_accuracy: 0.6000\n",
            "Epoch 429/500\n",
            "107/107 [==============================] - 0s 382us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 430/500\n",
            "107/107 [==============================] - 0s 400us/sample - loss: 0.0018 - accuracy: 0.7290 - val_loss: 0.0064 - val_accuracy: 0.6500\n",
            "Epoch 431/500\n",
            "107/107 [==============================] - 0s 375us/sample - loss: 0.0016 - accuracy: 0.7290 - val_loss: 0.0063 - val_accuracy: 0.6000\n",
            "Epoch 432/500\n",
            "107/107 [==============================] - 0s 353us/sample - loss: 0.0018 - accuracy: 0.7477 - val_loss: 0.0059 - val_accuracy: 0.6500\n",
            "Epoch 433/500\n",
            "107/107 [==============================] - 0s 369us/sample - loss: 0.0016 - accuracy: 0.7477 - val_loss: 0.0056 - val_accuracy: 0.6000\n",
            "Epoch 434/500\n",
            "107/107 [==============================] - 0s 350us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0063 - val_accuracy: 0.6500\n",
            "Epoch 435/500\n",
            "107/107 [==============================] - 0s 345us/sample - loss: 0.0016 - accuracy: 0.7477 - val_loss: 0.0058 - val_accuracy: 0.6000\n",
            "Epoch 436/500\n",
            "107/107 [==============================] - 0s 343us/sample - loss: 0.0016 - accuracy: 0.7757 - val_loss: 0.0066 - val_accuracy: 0.6500\n",
            "Epoch 437/500\n",
            "107/107 [==============================] - 0s 360us/sample - loss: 0.0016 - accuracy: 0.7477 - val_loss: 0.0061 - val_accuracy: 0.6000\n",
            "Epoch 438/500\n",
            "107/107 [==============================] - 0s 356us/sample - loss: 0.0016 - accuracy: 0.7570 - val_loss: 0.0064 - val_accuracy: 0.6000\n",
            "Epoch 439/500\n",
            "107/107 [==============================] - 0s 375us/sample - loss: 0.0018 - accuracy: 0.7477 - val_loss: 0.0054 - val_accuracy: 0.6000\n",
            "Epoch 440/500\n",
            "107/107 [==============================] - 0s 359us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0059 - val_accuracy: 0.6000\n",
            "Epoch 441/500\n",
            "107/107 [==============================] - 0s 373us/sample - loss: 0.0018 - accuracy: 0.7477 - val_loss: 0.0057 - val_accuracy: 0.6000\n",
            "Epoch 442/500\n",
            "107/107 [==============================] - 0s 386us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0064 - val_accuracy: 0.6000\n",
            "Epoch 443/500\n",
            "107/107 [==============================] - 0s 350us/sample - loss: 0.0017 - accuracy: 0.7290 - val_loss: 0.0065 - val_accuracy: 0.6000\n",
            "Epoch 444/500\n",
            "107/107 [==============================] - 0s 361us/sample - loss: 0.0020 - accuracy: 0.7196 - val_loss: 0.0069 - val_accuracy: 0.6000\n",
            "Epoch 445/500\n",
            "107/107 [==============================] - 0s 361us/sample - loss: 0.0017 - accuracy: 0.7196 - val_loss: 0.0055 - val_accuracy: 0.6000\n",
            "Epoch 446/500\n",
            "107/107 [==============================] - 0s 314us/sample - loss: 0.0018 - accuracy: 0.7290 - val_loss: 0.0065 - val_accuracy: 0.6000\n",
            "Epoch 447/500\n",
            "107/107 [==============================] - 0s 327us/sample - loss: 0.0017 - accuracy: 0.7196 - val_loss: 0.0056 - val_accuracy: 0.6000\n",
            "Epoch 448/500\n",
            "107/107 [==============================] - 0s 346us/sample - loss: 0.0016 - accuracy: 0.7570 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 449/500\n",
            "107/107 [==============================] - 0s 438us/sample - loss: 0.0016 - accuracy: 0.7664 - val_loss: 0.0066 - val_accuracy: 0.5500\n",
            "Epoch 450/500\n",
            "107/107 [==============================] - 0s 345us/sample - loss: 0.0019 - accuracy: 0.6822 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 451/500\n",
            "107/107 [==============================] - 0s 336us/sample - loss: 0.0017 - accuracy: 0.7850 - val_loss: 0.0062 - val_accuracy: 0.6000\n",
            "Epoch 452/500\n",
            "107/107 [==============================] - 0s 313us/sample - loss: 0.0018 - accuracy: 0.7290 - val_loss: 0.0057 - val_accuracy: 0.6500\n",
            "Epoch 453/500\n",
            "107/107 [==============================] - 0s 342us/sample - loss: 0.0016 - accuracy: 0.7944 - val_loss: 0.0061 - val_accuracy: 0.5500\n",
            "Epoch 454/500\n",
            "107/107 [==============================] - 0s 333us/sample - loss: 0.0016 - accuracy: 0.7664 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 455/500\n",
            "107/107 [==============================] - 0s 350us/sample - loss: 0.0016 - accuracy: 0.7757 - val_loss: 0.0070 - val_accuracy: 0.6000\n",
            "Epoch 456/500\n",
            "107/107 [==============================] - 0s 362us/sample - loss: 0.0016 - accuracy: 0.7383 - val_loss: 0.0057 - val_accuracy: 0.7000\n",
            "Epoch 457/500\n",
            "107/107 [==============================] - 0s 319us/sample - loss: 0.0016 - accuracy: 0.7570 - val_loss: 0.0066 - val_accuracy: 0.6000\n",
            "Epoch 458/500\n",
            "107/107 [==============================] - 0s 325us/sample - loss: 0.0018 - accuracy: 0.7196 - val_loss: 0.0059 - val_accuracy: 0.6500\n",
            "Epoch 459/500\n",
            "107/107 [==============================] - 0s 317us/sample - loss: 0.0018 - accuracy: 0.7664 - val_loss: 0.0071 - val_accuracy: 0.5500\n",
            "Epoch 460/500\n",
            "107/107 [==============================] - 0s 308us/sample - loss: 0.0018 - accuracy: 0.7009 - val_loss: 0.0061 - val_accuracy: 0.6500\n",
            "Epoch 461/500\n",
            "107/107 [==============================] - 0s 311us/sample - loss: 0.0016 - accuracy: 0.7757 - val_loss: 0.0069 - val_accuracy: 0.6000\n",
            "Epoch 462/500\n",
            "107/107 [==============================] - 0s 303us/sample - loss: 0.0017 - accuracy: 0.7290 - val_loss: 0.0061 - val_accuracy: 0.7000\n",
            "Epoch 463/500\n",
            "107/107 [==============================] - 0s 329us/sample - loss: 0.0017 - accuracy: 0.7664 - val_loss: 0.0064 - val_accuracy: 0.6000\n",
            "Epoch 464/500\n",
            "107/107 [==============================] - 0s 299us/sample - loss: 0.0017 - accuracy: 0.7664 - val_loss: 0.0065 - val_accuracy: 0.6500\n",
            "Epoch 465/500\n",
            "107/107 [==============================] - 0s 288us/sample - loss: 0.0015 - accuracy: 0.8037 - val_loss: 0.0061 - val_accuracy: 0.6000\n",
            "Epoch 466/500\n",
            "107/107 [==============================] - 0s 307us/sample - loss: 0.0016 - accuracy: 0.7850 - val_loss: 0.0061 - val_accuracy: 0.6000\n",
            "Epoch 467/500\n",
            "107/107 [==============================] - 0s 300us/sample - loss: 0.0015 - accuracy: 0.7944 - val_loss: 0.0068 - val_accuracy: 0.6000\n",
            "Epoch 468/500\n",
            "107/107 [==============================] - 0s 275us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0062 - val_accuracy: 0.6500\n",
            "Epoch 469/500\n",
            "107/107 [==============================] - 0s 309us/sample - loss: 0.0017 - accuracy: 0.7757 - val_loss: 0.0065 - val_accuracy: 0.6000\n",
            "Epoch 470/500\n",
            "107/107 [==============================] - 0s 319us/sample - loss: 0.0019 - accuracy: 0.7103 - val_loss: 0.0061 - val_accuracy: 0.6500\n",
            "Epoch 471/500\n",
            "107/107 [==============================] - 0s 278us/sample - loss: 0.0017 - accuracy: 0.7570 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 472/500\n",
            "107/107 [==============================] - 0s 284us/sample - loss: 0.0017 - accuracy: 0.7664 - val_loss: 0.0066 - val_accuracy: 0.6000\n",
            "Epoch 473/500\n",
            "107/107 [==============================] - 0s 302us/sample - loss: 0.0019 - accuracy: 0.7477 - val_loss: 0.0064 - val_accuracy: 0.5000\n",
            "Epoch 474/500\n",
            "107/107 [==============================] - 0s 297us/sample - loss: 0.0016 - accuracy: 0.7290 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 475/500\n",
            "107/107 [==============================] - 0s 296us/sample - loss: 0.0017 - accuracy: 0.7664 - val_loss: 0.0061 - val_accuracy: 0.6000\n",
            "Epoch 476/500\n",
            "107/107 [==============================] - 0s 330us/sample - loss: 0.0018 - accuracy: 0.7383 - val_loss: 0.0062 - val_accuracy: 0.7000\n",
            "Epoch 477/500\n",
            "107/107 [==============================] - 0s 350us/sample - loss: 0.0017 - accuracy: 0.7290 - val_loss: 0.0062 - val_accuracy: 0.5500\n",
            "Epoch 478/500\n",
            "107/107 [==============================] - 0s 311us/sample - loss: 0.0018 - accuracy: 0.7103 - val_loss: 0.0060 - val_accuracy: 0.6000\n",
            "Epoch 479/500\n",
            "107/107 [==============================] - 0s 300us/sample - loss: 0.0017 - accuracy: 0.7850 - val_loss: 0.0069 - val_accuracy: 0.6000\n",
            "Epoch 480/500\n",
            "107/107 [==============================] - 0s 271us/sample - loss: 0.0017 - accuracy: 0.7477 - val_loss: 0.0058 - val_accuracy: 0.6000\n",
            "Epoch 481/500\n",
            "107/107 [==============================] - 0s 282us/sample - loss: 0.0015 - accuracy: 0.7477 - val_loss: 0.0068 - val_accuracy: 0.5500\n",
            "Epoch 482/500\n",
            "107/107 [==============================] - 0s 282us/sample - loss: 0.0018 - accuracy: 0.7570 - val_loss: 0.0062 - val_accuracy: 0.6000\n",
            "Epoch 483/500\n",
            "107/107 [==============================] - 0s 398us/sample - loss: 0.0017 - accuracy: 0.7570 - val_loss: 0.0061 - val_accuracy: 0.6500\n",
            "Epoch 484/500\n",
            "107/107 [==============================] - 0s 345us/sample - loss: 0.0019 - accuracy: 0.7570 - val_loss: 0.0061 - val_accuracy: 0.6000\n",
            "Epoch 485/500\n",
            "107/107 [==============================] - 0s 321us/sample - loss: 0.0019 - accuracy: 0.7290 - val_loss: 0.0069 - val_accuracy: 0.6000\n",
            "Epoch 486/500\n",
            "107/107 [==============================] - 0s 299us/sample - loss: 0.0019 - accuracy: 0.7664 - val_loss: 0.0074 - val_accuracy: 0.6000\n",
            "Epoch 487/500\n",
            "107/107 [==============================] - 0s 302us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0057 - val_accuracy: 0.6000\n",
            "Epoch 488/500\n",
            "107/107 [==============================] - 0s 293us/sample - loss: 0.0018 - accuracy: 0.7103 - val_loss: 0.0068 - val_accuracy: 0.6000\n",
            "Epoch 489/500\n",
            "107/107 [==============================] - 0s 297us/sample - loss: 0.0021 - accuracy: 0.6822 - val_loss: 0.0063 - val_accuracy: 0.5500\n",
            "Epoch 490/500\n",
            "107/107 [==============================] - 0s 287us/sample - loss: 0.0017 - accuracy: 0.7757 - val_loss: 0.0066 - val_accuracy: 0.6000\n",
            "Epoch 491/500\n",
            "107/107 [==============================] - 0s 300us/sample - loss: 0.0019 - accuracy: 0.7103 - val_loss: 0.0063 - val_accuracy: 0.5000\n",
            "Epoch 492/500\n",
            "107/107 [==============================] - 0s 318us/sample - loss: 0.0015 - accuracy: 0.7664 - val_loss: 0.0062 - val_accuracy: 0.6000\n",
            "Epoch 493/500\n",
            "107/107 [==============================] - 0s 315us/sample - loss: 0.0016 - accuracy: 0.7477 - val_loss: 0.0060 - val_accuracy: 0.5500\n",
            "Epoch 494/500\n",
            "107/107 [==============================] - 0s 309us/sample - loss: 0.0015 - accuracy: 0.7944 - val_loss: 0.0054 - val_accuracy: 0.6000\n",
            "Epoch 495/500\n",
            "107/107 [==============================] - 0s 330us/sample - loss: 0.0016 - accuracy: 0.7383 - val_loss: 0.0063 - val_accuracy: 0.6000\n",
            "Epoch 496/500\n",
            "107/107 [==============================] - 0s 299us/sample - loss: 0.0017 - accuracy: 0.7477 - val_loss: 0.0063 - val_accuracy: 0.6000\n",
            "Epoch 497/500\n",
            "107/107 [==============================] - 0s 308us/sample - loss: 0.0017 - accuracy: 0.7477 - val_loss: 0.0067 - val_accuracy: 0.6000\n",
            "Epoch 498/500\n",
            "107/107 [==============================] - 0s 291us/sample - loss: 0.0016 - accuracy: 0.7477 - val_loss: 0.0063 - val_accuracy: 0.6000\n",
            "Epoch 499/500\n",
            "107/107 [==============================] - 0s 294us/sample - loss: 0.0016 - accuracy: 0.7570 - val_loss: 0.0064 - val_accuracy: 0.5500\n",
            "Epoch 500/500\n",
            "107/107 [==============================] - 0s 366us/sample - loss: 0.0017 - accuracy: 0.7383 - val_loss: 0.0060 - val_accuracy: 0.6000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1d6d3e7940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3OJLNwuZxtk",
        "colab_type": "code",
        "outputId": "475bf29b-c0e6-45ad-afb2-595d784bfa82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Predictions\n",
        "pred_train = model.predict(standardized_X_train) \n",
        "pred_test = model.predict(standardized_X_test)\n",
        "print (f\"sample probability: {pred_test[0]}\")\n",
        "pred_train = np.argmax(pred_train, axis=1)\n",
        "pred_test = np.argmax(pred_test, axis=1)\n",
        "print (f\"sample class: {pred_test[0]}\")"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample probability: [0.00588787 0.7134166  0.28069556]\n",
            "sample class: 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDoiOTgxdgLd",
        "colab_type": "code",
        "outputId": "607c171c-ec66-491f-b019-33941374b986",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Accuracy\n",
        "train_acc = accuracy_score(y_train, pred_train)\n",
        "test_acc = accuracy_score(y_test, pred_test)\n",
        "print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 0.78, test acc: 0.26\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LU9Wzt0ZxwI",
        "colab_type": "code",
        "outputId": "0f319956-83d3-4ce3-cdf9-b0fe07fb9025",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "# Classification report\n",
        "plot_confusion_matrix(y_true=y_test, y_pred=pred_test, classes=classes)\n",
        "print (classification_report(y_test, pred_test))"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Fvx4deO/TiRQuWiLBmJy58+Dt7Y2Xlxdtnu3Cnp1/xVtvjGHyl2Po/uoAvhk7mr6DRvDk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\nKaUs7f9w1bDvdAfEqwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.25      0.33         8\n",
            "           1       0.25      0.25      0.25         8\n",
            "           2       0.18      0.29      0.22         7\n",
            "\n",
            "    accuracy                           0.26        23\n",
            "   macro avg       0.31      0.26      0.27        23\n",
            "weighted avg       0.32      0.26      0.27        23\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpSoAEdGWku5",
        "colab_type": "code",
        "outputId": "530074c9-40ad-4899-9ea4-bbdaebfe2d6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qDM9aGERCUYbOjRKN6NS1eOZkFzeVpZ5a+gOj8Ul5N1wuo0lZoZgvJDXjrc5g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\nhlhaogSnEzJEE09HPiK+M7K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sOLE8E7ZgEN/F9HWYRtRg+JU+GtcvT/9kQ6qyiwpjZFTH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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yppKnFIL6Vv7",
        "colab_type": "text"
      },
      "source": [
        "It's important that we experiment, starting with simple models that underfit (high bias) and improve it towards a good fit. Starting with simple models (linear/logistic regression) let's us catch errors without the added complexity of more sophisticated models (neural networks). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WC0m-lMnE4Jp",
        "colab_type": "text"
      },
      "source": [
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/basic_ml/06_Multilayer_Perceptron/fit.png\" width=\"700\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xk5RXmKtFfD1",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "<div align=\"center\">\n",
        "\n",
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